--- title: "clika_runtime.ops functions" sidebar_label: "Functions" description: "Module-level functions of clika_runtime.ops." --- ## `abs` ```python absabs(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` abs(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor abs(input) -> Tensor The `abs` operator. ## `abs_` ```python abs_abs_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` abs_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor abs_(self) -> Tensor The `abs_` operator. ## `acos` ```python acosacos(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` acos(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor acos(input) -> Tensor The `acos` operator. ## `acos_` ```python acos_acos_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` acos_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor acos_(self) -> Tensor The `acos_` operator. ## `acosh` ```python acoshacosh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` acosh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor acosh(input) -> Tensor The `acosh` operator. ## `acosh_` ```python acosh_acosh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` acosh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor acosh_(self) -> Tensor The `acosh_` operator. ## `adaptive_avg_pool` ```python adaptive_avg_pooladaptive_avg_pool(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor ``` adaptive_avg_pool(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor adaptive_avg_pool(input, output_size) -> Tensor The `adaptive_avg_pool` operator. ## `adaptive_avg_pool1d` ```python adaptive_avg_pool1dadaptive_avg_pool1d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor ``` adaptive_avg_pool1d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor adaptive_avg_pool1d(input, output_size) -> Tensor The `adaptive_avg_pool1d` operator. ## `adaptive_avg_pool2d` ```python adaptive_avg_pool2dadaptive_avg_pool2d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor ``` adaptive_avg_pool2d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor adaptive_avg_pool2d(input, output_size) -> Tensor The `adaptive_avg_pool2d` operator. ## `adaptive_avg_pool3d` ```python adaptive_avg_pool3dadaptive_avg_pool3d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor ``` adaptive_avg_pool3d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor adaptive_avg_pool3d(input, output_size) -> Tensor The `adaptive_avg_pool3d` operator. ## `adaptive_max_pool` ```python adaptive_max_pooladaptive_max_pool(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor ``` adaptive_max_pool(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor adaptive_max_pool(input, output_size) -> Tensor The `adaptive_max_pool` operator. ## `adaptive_max_pool1d` ```python adaptive_max_pool1dadaptive_max_pool1d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor ``` adaptive_max_pool1d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor adaptive_max_pool1d(input, output_size) -> Tensor The `adaptive_max_pool1d` operator. ## `adaptive_max_pool2d` ```python adaptive_max_pool2dadaptive_max_pool2d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor ``` adaptive_max_pool2d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor adaptive_max_pool2d(input, output_size) -> Tensor The `adaptive_max_pool2d` operator. ## `adaptive_max_pool3d` ```python adaptive_max_pool3dadaptive_max_pool3d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor ``` adaptive_max_pool3d(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor adaptive_max_pool3d(input, output_size) -> Tensor The `adaptive_max_pool3d` operator. ## `add` ```python addadd(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` add(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor add(input: clika_runtime._core.ops.Scalar, other: clika_runtime._core.Tensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor add(input, other, alpha=1.0, *, activation='identity') -> Tensor The `add` operator. ## `add_` ```python add_add_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` add_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor add_(self, other, alpha=1.0, *, activation='identity') -> Tensor The `add_` operator. ## `add_layer_norm` ```python add_layer_normadd_layer_norm(input: clika_runtime._core.Tensor, residual: clika_runtime._core.Tensor | None = None, post_residual: clika_runtime._core.Tensor | None = None, normalized_shape: collections.abc.Sequence[int] = [], skip_bias: clika_runtime._core.Tensor | None = None, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` add_layer_norm(input: clika_runtime._core.Tensor, residual: clika_runtime._core.Tensor | None = None, post_residual: clika_runtime._core.Tensor | None = None, normalized_shape: collections.abc.Sequence[int] = [], skip_bias: clika_runtime._core.Tensor | None = None, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] add_layer_norm(input, residual=None, post_residual=None, normalized_shape=[], skip_bias=None, weight=None, bias=None, eps=None, *, activation=None) -> tuple[Tensor, Tensor] The `add_layer_norm` operator. ## `add_rms_norm` ```python add_rms_normadd_rms_norm(input: clika_runtime._core.Tensor, residual: clika_runtime._core.Tensor | None = None, residual2: clika_runtime._core.Tensor | None = None, post_residual: clika_runtime._core.Tensor | None = None, normalized_shape: collections.abc.Sequence[int] = [], skip_bias: clika_runtime._core.Tensor | None = None, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` add_rms_norm(input: clika_runtime._core.Tensor, residual: clika_runtime._core.Tensor | None = None, residual2: clika_runtime._core.Tensor | None = None, post_residual: clika_runtime._core.Tensor | None = None, normalized_shape: collections.abc.Sequence[int] = [], skip_bias: clika_runtime._core.Tensor | None = None, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] add_rms_norm(input, residual=None, residual2=None, post_residual=None, normalized_shape=[], skip_bias=None, weight=None, bias=None, eps=None, *, activation=None) -> tuple[Tensor, Tensor] The `add_rms_norm` operator. ## `all` ```python allall(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor ``` all(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor all(input, dims=[], keepdim=False) -> Tensor The `all` operator. ## `allclose` ```python allcloseallclose(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> clika_runtime._core.Tensor ``` allclose(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> clika_runtime._core.Tensor allclose(input, other, rtol=1e-5, atol=1e-8, equal_nan=False) -> Tensor The `allclose` operator. ## `amax` ```python amaxamax(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor ``` amax(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor amax(input, dims=[], keepdim=False) -> Tensor The `amax` operator. ## `amin` ```python aminamin(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor ``` amin(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor amin(input, dims=[], keepdim=False) -> Tensor The `amin` operator. ## `aminmax` ```python aminmaxaminmax(input: clika_runtime._core.Tensor, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` aminmax(input: clika_runtime._core.Tensor, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] aminmax(input, dim=None, keepdim=False) -> tuple[Tensor, Tensor] The `aminmax` operator. ## `any` ```python anyany(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor ``` any(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor any(input, dims=[], keepdim=False) -> Tensor The `any` operator. ## `arange` ```python arangearange(start: clika_runtime._core.ops.ScalarOrTensor, end: clika_runtime._core.ops.ScalarOrTensor, step: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), *, dtype: clika_runtime._core.DataType = DataType.Int64, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` arange(start: clika_runtime._core.ops.ScalarOrTensor, end: clika_runtime._core.ops.ScalarOrTensor, step: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), *, dtype: clika_runtime._core.DataType = DataType.Int64, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor arange(start, end, step=1, *, dtype=Int64, device=None) -> Tensor The `arange` operator. ## `argmax` ```python argmaxargmax(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, index_dtype: clika_runtime._core.DataType = DataType.Int64) -> clika_runtime._core.Tensor ``` argmax(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, index_dtype: clika_runtime._core.DataType = DataType.Int64) -> clika_runtime._core.Tensor argmax(input, dims=[], keepdim=False, *, index_dtype=Int64) -> Tensor The `argmax` operator. ## `argmin` ```python argminargmin(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, index_dtype: clika_runtime._core.DataType = DataType.Int64) -> clika_runtime._core.Tensor ``` argmin(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, index_dtype: clika_runtime._core.DataType = DataType.Int64) -> clika_runtime._core.Tensor argmin(input, dims=[], keepdim=False, *, index_dtype=Int64) -> Tensor The `argmin` operator. ## `argsort` ```python argsortargsort(input: clika_runtime._core.Tensor, dim: int = -1, descending: bool = False, stable: bool = False) -> clika_runtime._core.Tensor ``` argsort(input: clika_runtime._core.Tensor, dim: int = -1, descending: bool = False, stable: bool = False) -> clika_runtime._core.Tensor argsort(input, dim=-1, descending=False, stable=False) -> Tensor The `argsort` operator. ## `asin` ```python asinasin(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` asin(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor asin(input) -> Tensor The `asin` operator. ## `asin_` ```python asin_asin_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` asin_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor asin_(self) -> Tensor The `asin_` operator. ## `asinh` ```python asinhasinh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` asinh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor asinh(input) -> Tensor The `asinh` operator. ## `asinh_` ```python asinh_asinh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` asinh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor asinh_(self) -> Tensor The `asinh_` operator. ## `atan` ```python atanatan(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` atan(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor atan(input) -> Tensor The `atan` operator. ## `atan2` ```python atan2atan2(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` atan2(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor atan2(input, other) -> Tensor The `atan2` operator. ## `atan2_` ```python atan2_atan2_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` atan2_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor atan2_(self, other) -> Tensor The `atan2_` operator. ## `atan_` ```python atan_atan_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` atan_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor atan_(self) -> Tensor The `atan_` operator. ## `atanh` ```python atanhatanh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` atanh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor atanh(input) -> Tensor The `atanh` operator. ## `atanh_` ```python atanh_atanh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` atanh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor atanh_(self) -> Tensor The `atanh_` operator. ## `atleast_1d` ```python atleast_1datleast_1d(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` atleast_1d(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor atleast_1d(input) -> Tensor The `atleast_1d` operator. ## `atleast_2d` ```python atleast_2datleast_2d(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` atleast_2d(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor atleast_2d(input) -> Tensor The `atleast_2d` operator. ## `atleast_3d` ```python atleast_3datleast_3d(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` atleast_3d(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor atleast_3d(input) -> Tensor The `atleast_3d` operator. ## `attention` ```python attentionattention(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, attn_mask: clika_runtime._core.Tensor | None = None, head_sink: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), kept_prefix: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` attention(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, attn_mask: clika_runtime._core.Tensor | None = None, head_sink: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), kept_prefix: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor attention(query, key, value, attn_mask=None, head_sink=None, is_causal=None, q_scale=None, softcap=None, sliding_window=None, smooth_softmax=None, k_scale=None, v_scale=None, kept_prefix=None) -> Tensor The `attention` operator. ## `attention_over_cache` ```python attention_over_cacheattention_over_cache(query: clika_runtime._core.Tensor, cache_key: clika_runtime._core.Tensor, cache_value: clika_runtime._core.Tensor, kvcache_start: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, cu_seqlens_k: clika_runtime._core.Tensor, max_seqlen_q: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), max_seqlen_k: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), rope_cos: clika_runtime._core.Tensor | None = None, rope_sin: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, attn_mask: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, rotary_mode: object | None = None, num_heads: int | None = None, kv_num_heads: int | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), head_sink: clika_runtime._core.Tensor | None = None, q_norm_gain: clika_runtime._core.Tensor | None = None, k_norm_gain: clika_runtime._core.Tensor | None = None, qk_norm_eps: float | None = None, slot_ids: clika_runtime._core.Tensor | None = None, kept_prefix: clika_runtime._core.Tensor | None = None, kv_position_offset: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` attention_over_cache(query: clika_runtime._core.Tensor, cache_key: clika_runtime._core.Tensor, cache_value: clika_runtime._core.Tensor, kvcache_start: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, cu_seqlens_k: clika_runtime._core.Tensor, max_seqlen_q: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), max_seqlen_k: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), rope_cos: clika_runtime._core.Tensor | None = None, rope_sin: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, attn_mask: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, rotary_mode: object | None = None, num_heads: int | None = None, kv_num_heads: int | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), head_sink: clika_runtime._core.Tensor | None = None, q_norm_gain: clika_runtime._core.Tensor | None = None, k_norm_gain: clika_runtime._core.Tensor | None = None, qk_norm_eps: float | None = None, slot_ids: clika_runtime._core.Tensor | None = None, kept_prefix: clika_runtime._core.Tensor | None = None, kv_position_offset: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor attention_over_cache(query, cache_key, cache_value, kvcache_start, cu_seqlens_q, cu_seqlens_k, max_seqlen_q=None, max_seqlen_k=None, rope_cos=None, rope_sin=None, position_ids=None, attn_mask=None, is_causal=None, q_scale=None, softcap=None, sliding_window=None, smooth_softmax=None, rotary_mode=None, num_heads=None, kv_num_heads=None, k_scale=None, v_scale=None, head_sink=None, q_norm_gain=None, k_norm_gain=None, qk_norm_eps=None, slot_ids=None, kept_prefix=None, kv_position_offset=None) -> Tensor The `attention_over_cache` operator. ## `attention_varlen` ```python attention_varlenattention_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, cu_seqlens_k: clika_runtime._core.Tensor, max_seqlen_q: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), max_seqlen_k: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), attn_mask: clika_runtime._core.Tensor | None = None, head_sink: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), kept_prefix: clika_runtime._core.Tensor | None = None, kv_position_offset: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` attention_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, cu_seqlens_k: clika_runtime._core.Tensor, max_seqlen_q: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), max_seqlen_k: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), attn_mask: clika_runtime._core.Tensor | None = None, head_sink: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), kept_prefix: clika_runtime._core.Tensor | None = None, kv_position_offset: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor attention_varlen(query, key, value, cu_seqlens_q, cu_seqlens_k, max_seqlen_q=None, max_seqlen_k=None, attn_mask=None, head_sink=None, is_causal=None, q_scale=None, softcap=None, sliding_window=None, smooth_softmax=None, k_scale=None, v_scale=None, kept_prefix=None, kv_position_offset=None) -> Tensor The `attention_varlen` operator. ## `avg_pool` ```python avg_poolavg_pool(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], ceil_mode: bool, count_include_pad: bool, divisor_override: int | None) -> clika_runtime._core.Tensor ``` avg_pool(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], ceil_mode: bool, count_include_pad: bool, divisor_override: int | None) -> clika_runtime._core.Tensor avg_pool(input, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override) -> Tensor The `avg_pool` operator. ## `avg_pool1d` ```python avg_pool1davg_pool1d(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0], ceil_mode: bool = False, count_include_pad: bool = True) -> clika_runtime._core.Tensor ``` avg_pool1d(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0], ceil_mode: bool = False, count_include_pad: bool = True) -> clika_runtime._core.Tensor avg_pool1d(input, kernel_size, stride=[], padding=[0], ceil_mode=False, count_include_pad=True) -> Tensor The `avg_pool1d` operator. ## `avg_pool2d` ```python avg_pool2davg_pool2d(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0], ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: int | None = None) -> clika_runtime._core.Tensor ``` avg_pool2d(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0], ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: int | None = None) -> clika_runtime._core.Tensor avg_pool2d(input, kernel_size, stride=[], padding=[0, 0], ceil_mode=False, count_include_pad=True, divisor_override=None) -> Tensor The `avg_pool2d` operator. ## `avg_pool3d` ```python avg_pool3davg_pool3d(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0, 0], ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: int | None = None) -> clika_runtime._core.Tensor ``` avg_pool3d(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0, 0], ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: int | None = None) -> clika_runtime._core.Tensor avg_pool3d(input, kernel_size, stride=[], padding=[0, 0, 0], ceil_mode=False, count_include_pad=True, divisor_override=None) -> Tensor The `avg_pool3d` operator. ## `batch_norm` ```python batch_normbatch_norm(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, running_mean: clika_runtime._core.Tensor | None = None, running_var: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor ``` batch_norm(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, running_mean: clika_runtime._core.Tensor | None = None, running_var: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor batch_norm(input, weight=None, bias=None, running_mean=None, running_var=None, eps=None, *, activation=None) -> Tensor The `batch_norm` operator. ## `bernoulli` ```python bernoullibernoulli(probabilities: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` bernoulli(probabilities: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor bernoulli(probabilities, *, device=None) -> Tensor The `bernoulli` operator. ## `bernoulli_` ```python bernoulli_bernoulli_(self: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> None ``` bernoulli_(self: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> None bernoulli_(self, *, device=None) -> None The `bernoulli_` operator. ## `binary_cross_entropy` ```python binary_cross_entropybinary_cross_entropy(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, reduction: object = 'mean') -> clika_runtime._core.Tensor ``` binary_cross_entropy(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, reduction: object = 'mean') -> clika_runtime._core.Tensor binary_cross_entropy(input, target, weight=None, reduction='mean') -> Tensor The `binary_cross_entropy` operator. ## `binary_cross_entropy_with_logits` ```python binary_cross_entropy_with_logitsbinary_cross_entropy_with_logits(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, reduction: object = 'mean', pos_weight: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` binary_cross_entropy_with_logits(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, reduction: object = 'mean', pos_weight: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor binary_cross_entropy_with_logits(input, target, weight=None, reduction='mean', pos_weight=None) -> Tensor The `binary_cross_entropy_with_logits` operator. ## `bincount` ```python bincountbincount(input: clika_runtime._core.Tensor, weights: clika_runtime._core.Tensor | None = None, minlength: int = 0) -> clika_runtime._core.Tensor ``` bincount(input: clika_runtime._core.Tensor, weights: clika_runtime._core.Tensor | None = None, minlength: int = 0) -> clika_runtime._core.Tensor bincount(input, weights=None, minlength=0) -> Tensor The `bincount` operator. ## `bitwise_and` ```python bitwise_andbitwise_and(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` bitwise_and(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor bitwise_and(input, other) -> Tensor The `bitwise_and` operator. ## `bitwise_and_` ```python bitwise_and_bitwise_and_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` bitwise_and_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor bitwise_and_(self, other) -> Tensor The `bitwise_and_` operator. ## `bitwise_left_shift` ```python bitwise_left_shiftbitwise_left_shift(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` bitwise_left_shift(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor bitwise_left_shift(input, other) -> Tensor The `bitwise_left_shift` operator. ## `bitwise_left_shift_` ```python bitwise_left_shift_bitwise_left_shift_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` bitwise_left_shift_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor bitwise_left_shift_(self, other) -> Tensor The `bitwise_left_shift_` operator. ## `bitwise_not` ```python bitwise_notbitwise_not(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` bitwise_not(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor bitwise_not(input) -> Tensor The `bitwise_not` operator. ## `bitwise_not_` ```python bitwise_not_bitwise_not_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` bitwise_not_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor bitwise_not_(self) -> Tensor The `bitwise_not_` operator. ## `bitwise_or` ```python bitwise_orbitwise_or(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` bitwise_or(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor bitwise_or(input, other) -> Tensor The `bitwise_or` operator. ## `bitwise_or_` ```python bitwise_or_bitwise_or_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` bitwise_or_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor bitwise_or_(self, other) -> Tensor The `bitwise_or_` operator. ## `bitwise_right_shift` ```python bitwise_right_shiftbitwise_right_shift(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` bitwise_right_shift(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor bitwise_right_shift(input, other) -> Tensor The `bitwise_right_shift` operator. ## `bitwise_right_shift_` ```python bitwise_right_shift_bitwise_right_shift_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` bitwise_right_shift_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor bitwise_right_shift_(self, other) -> Tensor The `bitwise_right_shift_` operator. ## `bitwise_xor` ```python bitwise_xorbitwise_xor(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` bitwise_xor(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor bitwise_xor(input, other) -> Tensor The `bitwise_xor` operator. ## `bitwise_xor_` ```python bitwise_xor_bitwise_xor_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` bitwise_xor_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor bitwise_xor_(self, other) -> Tensor The `bitwise_xor_` operator. ## `bmm` ```python bmmbmm(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor ``` bmm(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor bmm(input, other, bias=None, *, activation=None) -> Tensor The `bmm` operator. ## `broadcast_tensors` ```python broadcast_tensorsbroadcast_tensors(tensors: collections.abc.Sequence[clika_runtime._core.Tensor]) -> list[clika_runtime._core.Tensor] ``` broadcast_tensors(tensors: collections.abc.Sequence[clika_runtime._core.Tensor]) -> list[clika_runtime._core.Tensor] broadcast_tensors(tensors) -> list[Tensor] The `broadcast_tensors` operator. ## `broadcast_to` ```python broadcast_tobroadcast_to(input: clika_runtime._core.Tensor, shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor ``` broadcast_to(input: clika_runtime._core.Tensor, shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor broadcast_to(input, shape) -> Tensor The `broadcast_to` operator. ## `bucketize` ```python bucketizebucketize(input: clika_runtime._core.Tensor, boundaries: clika_runtime._core.Tensor, out_int32: bool = False, right: bool = False) -> clika_runtime._core.Tensor ``` bucketize(input: clika_runtime._core.Tensor, boundaries: clika_runtime._core.Tensor, out_int32: bool = False, right: bool = False) -> clika_runtime._core.Tensor bucketize(input, boundaries, out_int32=False, right=False) -> Tensor The `bucketize` operator. ## `cast` ```python castcast(input: clika_runtime._core.Tensor, target: clika_runtime._core.DataType, force_copy: bool = False) -> clika_runtime._core.Tensor ``` cast(input: clika_runtime._core.Tensor, target: clika_runtime._core.DataType, force_copy: bool = False) -> clika_runtime._core.Tensor cast(input, target, force_copy=False) -> Tensor The `cast` operator. ## `cast_like` ```python cast_likecast_like(input: clika_runtime._core.Tensor, reference: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` cast_like(input: clika_runtime._core.Tensor, reference: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor cast_like(input, reference) -> Tensor The `cast_like` operator. ## `causal_conv_update` ```python causal_conv_updatecausal_conv_update(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None, state: clika_runtime._core.Tensor, seq_lens: clika_runtime._core.Tensor | None = None, slot_ids: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor ``` causal_conv_update(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None, state: clika_runtime._core.Tensor, seq_lens: clika_runtime._core.Tensor | None = None, slot_ids: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor causal_conv_update(input, weight, bias, state, seq_lens=None, slot_ids=None, *, activation=None) -> Tensor The `causal_conv_update` operator. ## `cdist` ```python cdistcdist(x1: clika_runtime._core.Tensor, x2: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...)) -> clika_runtime._core.Tensor ``` cdist(x1: clika_runtime._core.Tensor, x2: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...)) -> clika_runtime._core.Tensor cdist(x1, x2, p=2.0) -> Tensor The `cdist` operator. ## `ceil` ```python ceilceil(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` ceil(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ceil(input) -> Tensor The `ceil` operator. ## `ceil_` ```python ceil_ceil_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` ceil_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ceil_(self) -> Tensor The `ceil_` operator. ## `celu` ```python celucelu(input: clika_runtime._core.Tensor, alpha: float = 1.0) -> clika_runtime._core.Tensor ``` celu(input: clika_runtime._core.Tensor, alpha: float = 1.0) -> clika_runtime._core.Tensor celu(input, alpha=1.0) -> Tensor The `celu` operator. ## `celu_` ```python celu_celu_(self: clika_runtime._core.Tensor, alpha: float = 1.0) -> clika_runtime._core.Tensor ``` celu_(self: clika_runtime._core.Tensor, alpha: float = 1.0) -> clika_runtime._core.Tensor celu_(self, alpha=1.0) -> Tensor The `celu_` operator. ## `chunk` ```python chunkchunk(input: clika_runtime._core.Tensor, num_chunks: int, dim: int = 0) -> list[clika_runtime._core.Tensor] ``` chunk(input: clika_runtime._core.Tensor, num_chunks: int, dim: int = 0) -> list[clika_runtime._core.Tensor] chunk(input, num_chunks, dim=0) -> list[Tensor] The `chunk` operator. ## `circular_pad` ```python circular_padcircular_pad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor ``` circular_pad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor circular_pad(input, pad) -> Tensor The `circular_pad` operator. ## `clamp` ```python clampclamp(input: clika_runtime._core.Tensor, min: clika_runtime._core.ops.ScalarOrTensor | None = None, max: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor ``` clamp(input: clika_runtime._core.Tensor, min: clika_runtime._core.ops.ScalarOrTensor | None = None, max: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor clamp(input, min=None, max=None) -> Tensor The `clamp` operator. ## `clamp_` ```python clamp_clamp_(self: clika_runtime._core.Tensor, min: clika_runtime._core.ops.ScalarOrTensor | None = None, max: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor ``` clamp_(self: clika_runtime._core.Tensor, min: clika_runtime._core.ops.ScalarOrTensor | None = None, max: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor clamp_(self, min=None, max=None) -> Tensor The `clamp_` operator. ## `clamp_max` ```python clamp_maxclamp_max(input: clika_runtime._core.Tensor, max: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` clamp_max(input: clika_runtime._core.Tensor, max: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor clamp_max(input, max) -> Tensor The `clamp_max` operator. ## `clamp_max_` ```python clamp_max_clamp_max_(self: clika_runtime._core.Tensor, max: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` clamp_max_(self: clika_runtime._core.Tensor, max: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor clamp_max_(self, max) -> Tensor The `clamp_max_` operator. ## `clamp_min` ```python clamp_minclamp_min(input: clika_runtime._core.Tensor, min: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` clamp_min(input: clika_runtime._core.Tensor, min: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor clamp_min(input, min) -> Tensor The `clamp_min` operator. ## `clamp_min_` ```python clamp_min_clamp_min_(self: clika_runtime._core.Tensor, min: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` clamp_min_(self: clika_runtime._core.Tensor, min: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor clamp_min_(self, min) -> Tensor The `clamp_min_` operator. ## `clone` ```python cloneclone(src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` clone(src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor clone(src) -> Tensor The `clone` operator. ## `concat` ```python concatconcat(tensors: collections.abc.Sequence[clika_runtime._core.Tensor], dim: int = 0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` concat(tensors: collections.abc.Sequence[clika_runtime._core.Tensor], dim: int = 0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor concat(tensors, dim=0, *, activation='identity') -> Tensor The `concat` operator. ## `constant_pad` ```python constant_padconstant_pad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], value: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor ``` constant_pad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], value: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor constant_pad(input, pad, value=None) -> Tensor The `constant_pad` operator. ## `contiguous` ```python contiguouscontiguous(input: clika_runtime._core.Tensor, force_copy: bool = False) -> clika_runtime._core.Tensor ``` contiguous(input: clika_runtime._core.Tensor, force_copy: bool = False) -> clika_runtime._core.Tensor contiguous(input, force_copy=False) -> Tensor The `contiguous` operator. ## `conv` ```python convconv(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` conv(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor conv(input, weight, bias, stride, padding, dilation, groups, mode='constant', value=None, *, activation='identity') -> Tensor The `conv` operator. ## `conv1d` ```python conv1dconv1d(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` conv1d(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor conv1d(input, weight, bias=None, stride=[1], padding=[0], dilation=[1], groups=1, *, activation='identity') -> Tensor The `conv1d` operator. ## `conv2d` ```python conv2dconv2d(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` conv2d(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor conv2d(input, weight, bias=None, stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, *, activation='identity') -> Tensor The `conv2d` operator. ## `conv3d` ```python conv3dconv3d(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` conv3d(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor conv3d(input, weight, bias=None, stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], groups=1, *, activation='identity') -> Tensor The `conv3d` operator. ## `conv_transpose` ```python conv_transposeconv_transpose(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], groups: int, dilation: collections.abc.Sequence[int], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` conv_transpose(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], groups: int, dilation: collections.abc.Sequence[int], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor conv_transpose(input, weight, bias, stride, padding, output_padding, groups, dilation, *, activation='identity') -> Tensor The `conv_transpose` operator. ## `conv_transpose1d` ```python conv_transpose1dconv_transpose1d(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0, 0], output_padding: collections.abc.Sequence[int] = [0], groups: int = 1, dilation: collections.abc.Sequence[int] = [1], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` conv_transpose1d(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0, 0], output_padding: collections.abc.Sequence[int] = [0], groups: int = 1, dilation: collections.abc.Sequence[int] = [1], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor conv_transpose1d(input, weight, bias=None, stride=[1], padding=[0, 0], output_padding=[0], groups=1, dilation=[1], *, activation='identity') -> Tensor The `conv_transpose1d` operator. ## `conv_transpose2d` ```python conv_transpose2dconv_transpose2d(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0], groups: int = 1, dilation: collections.abc.Sequence[int] = [1, 1], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` conv_transpose2d(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0], groups: int = 1, dilation: collections.abc.Sequence[int] = [1, 1], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor conv_transpose2d(input, weight, bias=None, stride=[1, 1], padding=[0, 0, 0, 0], output_padding=[0, 0], groups=1, dilation=[1, 1], *, activation='identity') -> Tensor The `conv_transpose2d` operator. ## `conv_transpose3d` ```python conv_transpose3dconv_transpose3d(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0, 0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0, 0], groups: int = 1, dilation: collections.abc.Sequence[int] = [1, 1, 1], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` conv_transpose3d(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0, 0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0, 0], groups: int = 1, dilation: collections.abc.Sequence[int] = [1, 1, 1], *, activation: object | None = 'identity') -> clika_runtime._core.Tensor conv_transpose3d(input, weight, bias=None, stride=[1, 1, 1], padding=[0, 0, 0, 0, 0, 0], output_padding=[0, 0, 0], groups=1, dilation=[1, 1, 1], *, activation='identity') -> Tensor The `conv_transpose3d` operator. ## `copy` ```python copycopy(src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...), force_copy: bool = True) -> clika_runtime._core.Tensor ``` copy(src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...), force_copy: bool = True) -> clika_runtime._core.Tensor copy(src, *, target=None, force_copy=True) -> Tensor The `copy` operator. ## `copy_` ```python copy_copy_(self: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` copy_(self: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor copy_(self, src, *, target=None) -> Tensor The `copy_` operator. ## `copy_into` ```python copy_intocopy_into(out: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` copy_into(out: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor copy_into(out, src, *, target=None) -> Tensor The `copy_into` operator. ## `copy_to_cpu` ```python copy_to_cpucopy_to_cpu(src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` copy_to_cpu(src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor copy_to_cpu(src) -> Tensor The `copy_to_cpu` operator. ## `copysign` ```python copysigncopysign(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` copysign(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor copysign(input, other) -> Tensor The `copysign` operator. ## `copysign_` ```python copysign_copysign_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` copysign_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor copysign_(self, other) -> Tensor The `copysign_` operator. ## `cos` ```python coscos(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` cos(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor cos(input) -> Tensor The `cos` operator. ## `cos_` ```python cos_cos_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` cos_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor cos_(self) -> Tensor The `cos_` operator. ## `cosh` ```python coshcosh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` cosh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor cosh(input) -> Tensor The `cosh` operator. ## `cosh_` ```python cosh_cosh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` cosh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor cosh_(self) -> Tensor The `cosh_` operator. ## `cosine_similarity` ```python cosine_similaritycosine_similarity(x1: clika_runtime._core.Tensor, x2: clika_runtime._core.Tensor, dim: int = 1, eps: float | None = None) -> clika_runtime._core.Tensor ``` cosine_similarity(x1: clika_runtime._core.Tensor, x2: clika_runtime._core.Tensor, dim: int = 1, eps: float | None = None) -> clika_runtime._core.Tensor cosine_similarity(x1, x2, dim=1, eps=None) -> Tensor The `cosine_similarity` operator. ## `count_nonzero` ```python count_nonzerocount_nonzero(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor ``` count_nonzero(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor count_nonzero(input, dims=[]) -> Tensor The `count_nonzero` operator. ## `cross` ```python crosscross(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor ``` cross(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor cross(input, other, dim=None) -> Tensor The `cross` operator. ## `cross_entropy` ```python cross_entropycross_entropy(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, ignore_index: int | None = None, reduction: object = 'mean') -> clika_runtime._core.Tensor ``` cross_entropy(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, ignore_index: int | None = None, reduction: object = 'mean') -> clika_runtime._core.Tensor cross_entropy(input, target, weight=None, ignore_index=None, reduction='mean') -> Tensor The `cross_entropy` operator. ## `cummax` ```python cummaxcummax(input: clika_runtime._core.Tensor, dim: int) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` cummax(input: clika_runtime._core.Tensor, dim: int) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] cummax(input, dim) -> tuple[Tensor, Tensor] The `cummax` operator. ## `cummin` ```python cummincummin(input: clika_runtime._core.Tensor, dim: int) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` cummin(input: clika_runtime._core.Tensor, dim: int) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] cummin(input, dim) -> tuple[Tensor, Tensor] The `cummin` operator. ## `cumprod` ```python cumprodcumprod(input: clika_runtime._core.Tensor, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor ``` cumprod(input: clika_runtime._core.Tensor, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor cumprod(input, dim, *, dtype=Undefined) -> Tensor The `cumprod` operator. ## `cumprod_` ```python cumprod_cumprod_(self: clika_runtime._core.Tensor, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor ``` cumprod_(self: clika_runtime._core.Tensor, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor cumprod_(self, dim, *, dtype=Undefined) -> Tensor The `cumprod_` operator. ## `cumsum` ```python cumsumcumsum(input: clika_runtime._core.Tensor, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor ``` cumsum(input: clika_runtime._core.Tensor, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor cumsum(input, dim, *, dtype=Undefined) -> Tensor The `cumsum` operator. ## `cumsum_` ```python cumsum_cumsum_(self: clika_runtime._core.Tensor, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor ``` cumsum_(self: clika_runtime._core.Tensor, dim: int, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor cumsum_(self, dim, *, dtype=Undefined) -> Tensor The `cumsum_` operator. ## `deform_conv` ```python deform_convdeform_conv(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, offset_groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` deform_conv(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, offset_groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor deform_conv(input, weight, offset, mask=None, bias=None, stride=[], padding=[], dilation=[], groups=1, offset_groups=1, *, activation='identity') -> Tensor The `deform_conv` operator. ## `deg2rad` ```python deg2raddeg2rad(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` deg2rad(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor deg2rad(input) -> Tensor The `deg2rad` operator. ## `deg2rad_` ```python deg2rad_deg2rad_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` deg2rad_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor deg2rad_(self) -> Tensor The `deg2rad_` operator. ## `dequantize` ```python dequantizedequantize(input: clika_runtime._core.QTensor, *, target_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor ``` dequantize(input: clika_runtime._core.QTensor, *, target_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor dequantize(input, *, target_dtype=Undefined) -> Tensor The `dequantize` operator. ## `dequantize_` ```python dequantize_dequantize_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` dequantize_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor dequantize_(input, out) -> Tensor The `dequantize_` operator. ## `diag` ```python diagdiag(input: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor ``` diag(input: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor diag(input, diagonal=0) -> Tensor The `diag` operator. ## `diag_embed` ```python diag_embeddiag_embed(input: clika_runtime._core.Tensor, offset: int = 0, dim1: int = -2, dim2: int = -1) -> clika_runtime._core.Tensor ``` diag_embed(input: clika_runtime._core.Tensor, offset: int = 0, dim1: int = -2, dim2: int = -1) -> clika_runtime._core.Tensor diag_embed(input, offset=0, dim1=-2, dim2=-1) -> Tensor The `diag_embed` operator. ## `diagonal` ```python diagonaldiagonal(input: clika_runtime._core.Tensor, offset: int = 0, dim1: int = 0, dim2: int = 1) -> clika_runtime._core.Tensor ``` diagonal(input: clika_runtime._core.Tensor, offset: int = 0, dim1: int = 0, dim2: int = 1) -> clika_runtime._core.Tensor diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor The `diagonal` operator. ## `diff` ```python diffdiff(input: clika_runtime._core.Tensor, n: int = 1, dim: int = -1, prepend: clika_runtime._core.Tensor | None = None, append: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` diff(input: clika_runtime._core.Tensor, n: int = 1, dim: int = -1, prepend: clika_runtime._core.Tensor | None = None, append: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor diff(input, n=1, dim=-1, prepend=None, append=None) -> Tensor The `diff` operator. ## `div` ```python divdiv(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, rounding_mode: object | None = 'none', *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` div(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, rounding_mode: object | None = 'none', *, activation: object | None = 'identity') -> clika_runtime._core.Tensor div(input: clika_runtime._core.ops.Scalar, other: clika_runtime._core.Tensor, rounding_mode: object | None = 'none', *, activation: object | None = 'identity') -> clika_runtime._core.Tensor div(input, other, rounding_mode='none', *, activation='identity') -> Tensor The `div` operator. ## `div_` ```python div_div_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, rounding_mode: object | None = 'none', *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` div_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, rounding_mode: object | None = 'none', *, activation: object | None = 'identity') -> clika_runtime._core.Tensor div_(self, other, rounding_mode='none', *, activation='identity') -> Tensor The `div_` operator. ## `dot` ```python dotdot(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` dot(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor dot(input, other) -> Tensor The `dot` operator. ## `dynamic_quantize` ```python dynamic_quantizedynamic_quantize(input: clika_runtime._core.Tensor) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` dynamic_quantize(input: clika_runtime._core.Tensor) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] dynamic_quantize(input) -> tuple[Tensor, Tensor, Tensor] The `dynamic_quantize` operator. ## `einsum` ```python einsumeinsum(equation: str, operands: collections.abc.Sequence[clika_runtime._core.Tensor]) -> clika_runtime._core.Tensor ``` einsum(equation: str, operands: collections.abc.Sequence[clika_runtime._core.Tensor]) -> clika_runtime._core.Tensor einsum(equation, operands) -> Tensor The `einsum` operator. ## `elu` ```python eluelu(input: clika_runtime._core.Tensor, alpha: float = 1.0, scale: float = 1.0, input_scale: float = 1.0) -> clika_runtime._core.Tensor ``` elu(input: clika_runtime._core.Tensor, alpha: float = 1.0, scale: float = 1.0, input_scale: float = 1.0) -> clika_runtime._core.Tensor elu(input, alpha=1.0, scale=1.0, input_scale=1.0) -> Tensor The `elu` operator. ## `elu_` ```python elu_elu_(self: clika_runtime._core.Tensor, alpha: float = 1.0, scale: float = 1.0, input_scale: float = 1.0) -> clika_runtime._core.Tensor ``` elu_(self: clika_runtime._core.Tensor, alpha: float = 1.0, scale: float = 1.0, input_scale: float = 1.0) -> clika_runtime._core.Tensor elu_(self, alpha=1.0, scale=1.0, input_scale=1.0) -> Tensor The `elu_` operator. ## `embedding` ```python embeddingembedding(indices: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor ``` embedding(indices: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor embedding(indices, weight, bias=None, *, activation=None) -> Tensor The `embedding` operator. ## `empty` ```python emptyempty(shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], dtype: clika_runtime._core.DataType, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...), pinned_for: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` empty(shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], dtype: clika_runtime._core.DataType, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...), pinned_for: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor empty(shape, dtype, *, device=None, pinned_for=None) -> Tensor The `empty` operator. ## `empty_like` ```python empty_likeempty_like(reference: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` empty_like(reference: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor empty_like(reference, *, device=None) -> Tensor The `empty_like` operator. ## `empty_strided` ```python empty_stridedempty_strided(size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], dtype: clika_runtime._core.DataType, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` empty_strided(size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], dtype: clika_runtime._core.DataType, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor empty_strided(size, stride, dtype, *, device=None) -> Tensor The `empty_strided` operator. ## `eq` ```python eqeq(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` eq(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor eq(input, other) -> Tensor The `eq` operator. ## `eq_` ```python eq_eq_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` eq_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor eq_(self, other) -> Tensor The `eq_` operator. ## `erf` ```python erferf(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` erf(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor erf(input) -> Tensor The `erf` operator. ## `erf_` ```python erf_erf_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` erf_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor erf_(self) -> Tensor The `erf_` operator. ## `erfc` ```python erfcerfc(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` erfc(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor erfc(input) -> Tensor The `erfc` operator. ## `erfc_` ```python erfc_erfc_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` erfc_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor erfc_(self) -> Tensor The `erfc_` operator. ## `erfinv` ```python erfinverfinv(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` erfinv(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor erfinv(input) -> Tensor The `erfinv` operator. ## `erfinv_` ```python erfinv_erfinv_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` erfinv_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor erfinv_(self) -> Tensor The `erfinv_` operator. ## `exp` ```python expexp(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` exp(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor exp(input) -> Tensor The `exp` operator. ## `exp_` ```python exp_exp_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` exp_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor exp_(self) -> Tensor The `exp_` operator. ## `expand` ```python expandexpand(input: clika_runtime._core.Tensor, shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], bidirectional: bool = False) -> clika_runtime._core.Tensor ``` expand(input: clika_runtime._core.Tensor, shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], bidirectional: bool = False) -> clika_runtime._core.Tensor expand(input, shape, bidirectional=False) -> Tensor The `expand` operator. ## `expand_as` ```python expand_asexpand_as(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` expand_as(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor expand_as(input, other) -> Tensor The `expand_as` operator. ## `exponential_` ```python exponential_exponential_(self: clika_runtime._core.Tensor, lambd: float = 1.0, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` exponential_(self: clika_runtime._core.Tensor, lambd: float = 1.0, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor exponential_(self, lambd=1.0, *, device=None) -> Tensor The `exponential_` operator. ## `eye` ```python eyeeye(n: clika_runtime._core.ops.ScalarOrTensor, m: clika_runtime._core.ops.ScalarOrTensor | None = None, *, dtype: clika_runtime._core.DataType = DataType.Float32, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` eye(n: clika_runtime._core.ops.ScalarOrTensor, m: clika_runtime._core.ops.ScalarOrTensor | None = None, *, dtype: clika_runtime._core.DataType = DataType.Float32, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor eye(n, m=None, *, dtype=Float32, device=None) -> Tensor The `eye` operator. ## `fast_gelu` ```python fast_gelufast_gelu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` fast_gelu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor fast_gelu(input) -> Tensor The `fast_gelu` operator. ## `fill` ```python fillfill(input: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` fill(input: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor fill(input, value, *, device=None) -> Tensor The `fill` operator. ## `fill_` ```python fill_fill_(self: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` fill_(self: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor fill_(self, value, *, device=None) -> Tensor The `fill_` operator. ## `fill_diagonal` ```python fill_diagonalfill_diagonal(input: clika_runtime._core.Tensor, fill_value: clika_runtime._core.ops.Scalar, wrap: bool = False, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` fill_diagonal(input: clika_runtime._core.Tensor, fill_value: clika_runtime._core.ops.Scalar, wrap: bool = False, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor fill_diagonal(input, fill_value, wrap=False, *, device=None) -> Tensor The `fill_diagonal` operator. ## `fill_diagonal_` ```python fill_diagonal_fill_diagonal_(self: clika_runtime._core.Tensor, fill_value: clika_runtime._core.ops.Scalar, wrap: bool = False, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` fill_diagonal_(self: clika_runtime._core.Tensor, fill_value: clika_runtime._core.ops.Scalar, wrap: bool = False, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor fill_diagonal_(self, fill_value, wrap=False, *, device=None) -> Tensor The `fill_diagonal_` operator. ## `flatten` ```python flattenflatten(input: clika_runtime._core.Tensor, start_dim: int = 0, end_dim: int = -1) -> clika_runtime._core.Tensor ``` flatten(input: clika_runtime._core.Tensor, start_dim: int = 0, end_dim: int = -1) -> clika_runtime._core.Tensor flatten(input, start_dim=0, end_dim=-1) -> Tensor The `flatten` operator. ## `flip` ```python flipflip(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor ``` flip(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor flip(input, dims) -> Tensor The `flip` operator. ## `fliplr` ```python fliplrfliplr(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` fliplr(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor fliplr(input) -> Tensor The `fliplr` operator. ## `flipud` ```python flipudflipud(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` flipud(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor flipud(input) -> Tensor The `flipud` operator. ## `floor` ```python floorfloor(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` floor(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor floor(input) -> Tensor The `floor` operator. ## `floor_` ```python floor_floor_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` floor_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor floor_(self) -> Tensor The `floor_` operator. ## `floor_divide` ```python floor_dividefloor_divide(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` floor_divide(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor floor_divide(input, other) -> Tensor The `floor_divide` operator. ## `floor_divide_` ```python floor_divide_floor_divide_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` floor_divide_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor floor_divide_(self, other) -> Tensor The `floor_divide_` operator. ## `fmax` ```python fmaxfmax(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` fmax(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor fmax(input, other) -> Tensor The `fmax` operator. ## `fmin` ```python fminfmin(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` fmin(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor fmin(input, other) -> Tensor The `fmin` operator. ## `fmod` ```python fmodfmod(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` fmod(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor fmod(input, other) -> Tensor The `fmod` operator. ## `fmod_` ```python fmod_fmod_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` fmod_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor fmod_(self, other) -> Tensor The `fmod_` operator. ## `fold` ```python foldfold(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int], kernel_size: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], stride: collections.abc.Sequence[int] = [], mode: object = 'constant', value: float | None = None) -> clika_runtime._core.Tensor ``` fold(input: clika_runtime._core.Tensor, output_size: collections.abc.Sequence[int], kernel_size: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], stride: collections.abc.Sequence[int] = [], mode: object = 'constant', value: float | None = None) -> clika_runtime._core.Tensor fold(input, output_size, kernel_size, dilation=[], padding=[], stride=[], mode='constant', value=None) -> Tensor The `fold` operator. ## `frac` ```python fracfrac(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` frac(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor frac(input) -> Tensor The `frac` operator. ## `frac_` ```python frac_frac_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` frac_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor frac_(self) -> Tensor The `frac_` operator. ## `frexp` ```python frexpfrexp(input: clika_runtime._core.Tensor) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` frexp(input: clika_runtime._core.Tensor) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] frexp(input) -> tuple[Tensor, Tensor] The `frexp` operator. ## `full` ```python fullfull(shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], fill_value: clika_runtime._core.ops.ScalarOrTensor, dtype: clika_runtime._core.DataType, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...), pinned_for: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` full(shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], fill_value: clika_runtime._core.ops.ScalarOrTensor, dtype: clika_runtime._core.DataType, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...), pinned_for: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor full(shape, fill_value, dtype, *, device=None, pinned_for=None) -> Tensor The `full` operator. ## `full_like` ```python full_likefull_like(reference: clika_runtime._core.Tensor, fill_value: clika_runtime._core.ops.ScalarOrTensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` full_like(reference: clika_runtime._core.Tensor, fill_value: clika_runtime._core.ops.ScalarOrTensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor full_like(reference, fill_value, *, device=None) -> Tensor The `full_like` operator. ## `gated_delta_update` ```python gated_delta_updategated_delta_update(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, beta: clika_runtime._core.Tensor, gate: clika_runtime._core.Tensor, state: clika_runtime._core.Tensor, seq_lens: clika_runtime._core.Tensor | None = None, slot_ids: clika_runtime._core.Tensor | None = None, scale: float | None = None, gate_bias: clika_runtime._core.Tensor | None = None, gate_scale: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` gated_delta_update(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, beta: clika_runtime._core.Tensor, gate: clika_runtime._core.Tensor, state: clika_runtime._core.Tensor, seq_lens: clika_runtime._core.Tensor | None = None, slot_ids: clika_runtime._core.Tensor | None = None, scale: float | None = None, gate_bias: clika_runtime._core.Tensor | None = None, gate_scale: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor gated_delta_update(query, key, value, beta, gate, state, seq_lens=None, slot_ids=None, scale=None, gate_bias=None, gate_scale=None) -> Tensor The `gated_delta_update` operator. ## `gated_rms_norm` ```python gated_rms_normgated_rms_norm(input: clika_runtime._core.Tensor, gate: clika_runtime._core.Tensor, normalized_shape: collections.abc.Sequence[int], weight: clika_runtime._core.Tensor | None = None, eps: float | None = None) -> clika_runtime._core.Tensor ``` gated_rms_norm(input: clika_runtime._core.Tensor, gate: clika_runtime._core.Tensor, normalized_shape: collections.abc.Sequence[int], weight: clika_runtime._core.Tensor | None = None, eps: float | None = None) -> clika_runtime._core.Tensor gated_rms_norm(input, gate, normalized_shape, weight=None, eps=None) -> Tensor The `gated_rms_norm` operator. ## `gather` ```python gathergather(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` gather(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor gather(input, dim, index) -> Tensor The `gather` operator. ## `ge` ```python gege(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` ge(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ge(input, other) -> Tensor The `ge` operator. ## `ge_` ```python ge_ge_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` ge_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ge_(self, other) -> Tensor The `ge_` operator. ## `geglu` ```python geglugeglu(input: clika_runtime._core.Tensor, approximate: object | None = 'none') -> clika_runtime._core.Tensor ``` geglu(input: clika_runtime._core.Tensor, approximate: object | None = 'none') -> clika_runtime._core.Tensor geglu(input, approximate='none') -> Tensor The `geglu` operator. ## `gelu` ```python gelugelu(input: clika_runtime._core.Tensor, approximate: object | None = 'none') -> clika_runtime._core.Tensor ``` gelu(input: clika_runtime._core.Tensor, approximate: object | None = 'none') -> clika_runtime._core.Tensor gelu(input, approximate='none') -> Tensor The `gelu` operator. ## `gelu_` ```python gelu_gelu_(self: clika_runtime._core.Tensor, approximate: object | None = 'none') -> clika_runtime._core.Tensor ``` gelu_(self: clika_runtime._core.Tensor, approximate: object | None = 'none') -> clika_runtime._core.Tensor gelu_(self, approximate='none') -> Tensor The `gelu_` operator. ## `generate_rotary_cache` ```python generate_rotary_cachegenerate_rotary_cache(rotary_dim: int, max_positions: int, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...), yarn_truncate: bool | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` generate_rotary_cache(rotary_dim: int, max_positions: int, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...), yarn_truncate: bool | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] generate_rotary_cache(rotary_dim, max_positions, theta=None, scaling=None, scale=None, low_freq_factor=None, high_freq_factor=None, original_max_pos=None, beta_fast=None, beta_slow=None, freq_factors=None, *, device=None, yarn_truncate=None) -> tuple[Tensor, Tensor] The `generate_rotary_cache` operator. ## `glu` ```python gluglu(input: clika_runtime._core.Tensor, dim: int = -1) -> clika_runtime._core.Tensor ``` glu(input: clika_runtime._core.Tensor, dim: int = -1) -> clika_runtime._core.Tensor glu(input, dim=-1) -> Tensor The `glu` operator. ## `grid_sample` ```python grid_samplegrid_sample(input: clika_runtime._core.Tensor, grid: clika_runtime._core.Tensor, mode: object = 'bilinear', padding_mode: object = 'zeros', align_corners: bool = False) -> clika_runtime._core.Tensor ``` grid_sample(input: clika_runtime._core.Tensor, grid: clika_runtime._core.Tensor, mode: object = 'bilinear', padding_mode: object = 'zeros', align_corners: bool = False) -> clika_runtime._core.Tensor grid_sample(input, grid, mode='bilinear', padding_mode='zeros', align_corners=False) -> Tensor The `grid_sample` operator. ## `group_norm` ```python group_normgroup_norm(input: clika_runtime._core.Tensor, num_groups: int, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, running_mean: clika_runtime._core.Tensor | None = None, running_var: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor ``` group_norm(input: clika_runtime._core.Tensor, num_groups: int, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, running_mean: clika_runtime._core.Tensor | None = None, running_var: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor group_norm(input, num_groups, weight=None, bias=None, running_mean=None, running_var=None, eps=None, *, activation=None) -> Tensor The `group_norm` operator. ## `group_query_attention` ```python group_query_attentiongroup_query_attention(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, past_key: clika_runtime._core.Tensor | None = None, past_value: clika_runtime._core.Tensor | None = None, kvcache_start: clika_runtime._core.Tensor | None = None, rope_cos: clika_runtime._core.Tensor | None = None, rope_sin: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, attn_mask: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, rotary_mode: object | None = None, num_heads: int | None = None, kv_num_heads: int | None = None, out_present_key: clika_runtime._core.Tensor | None = None, out_present_value: clika_runtime._core.Tensor | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), head_sink: clika_runtime._core.Tensor | None = None, q_norm_gain: clika_runtime._core.Tensor | None = None, k_norm_gain: clika_runtime._core.Tensor | None = None, qk_norm_eps: float | None = None, slot_ids: clika_runtime._core.Tensor | None = None, kept_prefix: clika_runtime._core.Tensor | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` group_query_attention(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, past_key: clika_runtime._core.Tensor | None = None, past_value: clika_runtime._core.Tensor | None = None, kvcache_start: clika_runtime._core.Tensor | None = None, rope_cos: clika_runtime._core.Tensor | None = None, rope_sin: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, attn_mask: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, rotary_mode: object | None = None, num_heads: int | None = None, kv_num_heads: int | None = None, out_present_key: clika_runtime._core.Tensor | None = None, out_present_value: clika_runtime._core.Tensor | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), head_sink: clika_runtime._core.Tensor | None = None, q_norm_gain: clika_runtime._core.Tensor | None = None, k_norm_gain: clika_runtime._core.Tensor | None = None, qk_norm_eps: float | None = None, slot_ids: clika_runtime._core.Tensor | None = None, kept_prefix: clika_runtime._core.Tensor | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] group_query_attention(query, key, value, past_key=None, past_value=None, kvcache_start=None, rope_cos=None, rope_sin=None, position_ids=None, attn_mask=None, is_causal=None, q_scale=None, softcap=None, sliding_window=None, smooth_softmax=None, rotary_mode=None, num_heads=None, kv_num_heads=None, out_present_key=None, out_present_value=None, k_scale=None, v_scale=None, head_sink=None, q_norm_gain=None, k_norm_gain=None, qk_norm_eps=None, slot_ids=None, kept_prefix=None) -> tuple[Tensor, Tensor, Tensor] The `group_query_attention` operator. ## `group_query_attention_varlen` ```python group_query_attention_varlengroup_query_attention_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, cu_seqlens_k: clika_runtime._core.Tensor, max_seqlen_q: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), max_seqlen_k: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), past_key: clika_runtime._core.Tensor | None = None, past_value: clika_runtime._core.Tensor | None = None, kvcache_start: clika_runtime._core.Tensor | None = None, rope_cos: clika_runtime._core.Tensor | None = None, rope_sin: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, attn_mask: clika_runtime._core.Tensor | None = None, head_sink: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, rotary_mode: object | None = None, num_heads: int | None = None, kv_num_heads: int | None = None, out_present_key: clika_runtime._core.Tensor | None = None, out_present_value: clika_runtime._core.Tensor | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), q_norm_gain: clika_runtime._core.Tensor | None = None, k_norm_gain: clika_runtime._core.Tensor | None = None, qk_norm_eps: float | None = None, slot_ids: clika_runtime._core.Tensor | None = None, kept_prefix: clika_runtime._core.Tensor | None = None, kv_position_offset: clika_runtime._core.Tensor | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` group_query_attention_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, cu_seqlens_k: clika_runtime._core.Tensor, max_seqlen_q: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), max_seqlen_k: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), past_key: clika_runtime._core.Tensor | None = None, past_value: clika_runtime._core.Tensor | None = None, kvcache_start: clika_runtime._core.Tensor | None = None, rope_cos: clika_runtime._core.Tensor | None = None, rope_sin: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, attn_mask: clika_runtime._core.Tensor | None = None, head_sink: clika_runtime._core.Tensor | None = None, is_causal: bool | None = None, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), softcap: float | None = None, sliding_window: int | None = None, smooth_softmax: bool | None = None, rotary_mode: object | None = None, num_heads: int | None = None, kv_num_heads: int | None = None, out_present_key: clika_runtime._core.Tensor | None = None, out_present_value: clika_runtime._core.Tensor | None = None, k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), q_norm_gain: clika_runtime._core.Tensor | None = None, k_norm_gain: clika_runtime._core.Tensor | None = None, qk_norm_eps: float | None = None, slot_ids: clika_runtime._core.Tensor | None = None, kept_prefix: clika_runtime._core.Tensor | None = None, kv_position_offset: clika_runtime._core.Tensor | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] group_query_attention_varlen(query, key, value, cu_seqlens_q, cu_seqlens_k, max_seqlen_q=None, max_seqlen_k=None, past_key=None, past_value=None, kvcache_start=None, rope_cos=None, rope_sin=None, position_ids=None, attn_mask=None, head_sink=None, is_causal=None, q_scale=None, softcap=None, sliding_window=None, smooth_softmax=None, rotary_mode=None, num_heads=None, kv_num_heads=None, out_present_key=None, out_present_value=None, k_scale=None, v_scale=None, q_norm_gain=None, k_norm_gain=None, qk_norm_eps=None, slot_ids=None, kept_prefix=None, kv_position_offset=None) -> tuple[Tensor, Tensor, Tensor] The `group_query_attention_varlen` operator. ## `gt` ```python gtgt(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` gt(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor gt(input, other) -> Tensor The `gt` operator. ## `gt_` ```python gt_gt_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` gt_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor gt_(self, other) -> Tensor The `gt_` operator. ## `hardshrink` ```python hardshrinkhardshrink(input: clika_runtime._core.Tensor, lambd: float = 0.5) -> clika_runtime._core.Tensor ``` hardshrink(input: clika_runtime._core.Tensor, lambd: float = 0.5) -> clika_runtime._core.Tensor hardshrink(input, lambd=0.5) -> Tensor The `hardshrink` operator. ## `hardshrink_` ```python hardshrink_hardshrink_(self: clika_runtime._core.Tensor, lambd: float = 0.5) -> clika_runtime._core.Tensor ``` hardshrink_(self: clika_runtime._core.Tensor, lambd: float = 0.5) -> clika_runtime._core.Tensor hardshrink_(self, lambd=0.5) -> Tensor The `hardshrink_` operator. ## `hardsigmoid` ```python hardsigmoidhardsigmoid(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` hardsigmoid(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor hardsigmoid(input) -> Tensor The `hardsigmoid` operator. ## `hardsigmoid_` ```python hardsigmoid_hardsigmoid_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` hardsigmoid_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor hardsigmoid_(self) -> Tensor The `hardsigmoid_` operator. ## `hardswish` ```python hardswishhardswish(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` hardswish(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor hardswish(input) -> Tensor The `hardswish` operator. ## `hardswish_` ```python hardswish_hardswish_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` hardswish_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor hardswish_(self) -> Tensor The `hardswish_` operator. ## `hardtanh` ```python hardtanhhardtanh(input: clika_runtime._core.Tensor, min_val: float = -1.0, max_val: float = 1.0) -> clika_runtime._core.Tensor ``` hardtanh(input: clika_runtime._core.Tensor, min_val: float = -1.0, max_val: float = 1.0) -> clika_runtime._core.Tensor hardtanh(input, min_val=-1.0, max_val=1.0) -> Tensor The `hardtanh` operator. ## `hardtanh_` ```python hardtanh_hardtanh_(self: clika_runtime._core.Tensor, min_val: float = -1.0, max_val: float = 1.0) -> clika_runtime._core.Tensor ``` hardtanh_(self: clika_runtime._core.Tensor, min_val: float = -1.0, max_val: float = 1.0) -> clika_runtime._core.Tensor hardtanh_(self, min_val=-1.0, max_val=1.0) -> Tensor The `hardtanh_` operator. ## `hash_128` ```python hash_128hash_128(input: clika_runtime._core.Tensor, seed: int = 0) -> clika_runtime._core.Tensor ``` hash_128(input: clika_runtime._core.Tensor, seed: int = 0) -> clika_runtime._core.Tensor hash_128(input, seed=0) -> Tensor The `hash_128` operator. ## `hash_256` ```python hash_256hash_256(input: clika_runtime._core.Tensor, seed: int = 0) -> clika_runtime._core.Tensor ``` hash_256(input: clika_runtime._core.Tensor, seed: int = 0) -> clika_runtime._core.Tensor hash_256(input, seed=0) -> Tensor The `hash_256` operator. ## `hash_64` ```python hash_64hash_64(input: clika_runtime._core.Tensor, seed: int = 0) -> clika_runtime._core.Tensor ``` hash_64(input: clika_runtime._core.Tensor, seed: int = 0) -> clika_runtime._core.Tensor hash_64(input, seed=0) -> Tensor The `hash_64` operator. ## `hash_chain` ```python hash_chainhash_chain(rows: clika_runtime._core.Tensor, parent: clika_runtime._core.Tensor, seed: int = 0) -> clika_runtime._core.Tensor ``` hash_chain(rows: clika_runtime._core.Tensor, parent: clika_runtime._core.Tensor, seed: int = 0) -> clika_runtime._core.Tensor hash_chain(rows, parent, seed=0) -> Tensor The `hash_chain` operator. ## `hash_lanes` ```python hash_laneshash_lanes(input: clika_runtime._core.Tensor, key: int = 0, algo: object = 'auto', mask_bits: int = 0) -> clika_runtime._core.Tensor ``` hash_lanes(input: clika_runtime._core.Tensor, key: int = 0, algo: object = 'auto', mask_bits: int = 0) -> clika_runtime._core.Tensor hash_lanes(input, key=0, algo='auto', mask_bits=0) -> Tensor The `hash_lanes` operator. ## `hash_tensor` ```python hash_tensorhash_tensor(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, mode: object = 'xor_sum') -> clika_runtime._core.Tensor ``` hash_tensor(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, mode: object = 'xor_sum') -> clika_runtime._core.Tensor hash_tensor(input, dims=[], keepdim=False, mode='xor_sum') -> Tensor The `hash_tensor` operator. ## `histogram` ```python histogramhistogram(input: clika_runtime._core.Tensor, bins: int = 100, range: clika_runtime._core.Tensor | None = None, weight: clika_runtime._core.Tensor | None = None, density: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` histogram(input: clika_runtime._core.Tensor, bins: int = 100, range: clika_runtime._core.Tensor | None = None, weight: clika_runtime._core.Tensor | None = None, density: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] histogram(input, bins=100, range=None, weight=None, density=False) -> tuple[Tensor, Tensor] The `histogram` operator. ## `huber_loss` ```python huber_losshuber_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean', delta: float = 1.0) -> clika_runtime._core.Tensor ``` huber_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean', delta: float = 1.0) -> clika_runtime._core.Tensor huber_loss(input, target, reduction='mean', delta=1.0) -> Tensor The `huber_loss` operator. ## `hypot` ```python hypothypot(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` hypot(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor hypot(input, other) -> Tensor The `hypot` operator. ## `hypot_` ```python hypot_hypot_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` hypot_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor hypot_(self, other) -> Tensor The `hypot_` operator. ## `index` ```python indexindex(input: clika_runtime._core.Tensor, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry]) -> clika_runtime._core.Tensor ``` index(input: clika_runtime._core.Tensor, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry]) -> clika_runtime._core.Tensor index(input, indices) -> Tensor The `index` operator. ## `index_add` ```python index_addindex_add(input: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` index_add(input: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor index_add(input, dim, indices, src) -> Tensor The `index_add` operator. ## `index_add_` ```python index_add_index_add_(self: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` index_add_(self: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor index_add_(self, dim, indices, src) -> Tensor The `index_add_` operator. ## `index_copy` ```python index_copyindex_copy(input: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` index_copy(input: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor index_copy(input, dim, indices, src) -> Tensor The `index_copy` operator. ## `index_copy_` ```python index_copy_index_copy_(self: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` index_copy_(self: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor index_copy_(self, dim, indices, src) -> Tensor The `index_copy_` operator. ## `index_fill` ```python index_fillindex_fill(input: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` index_fill(input: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor index_fill(input, dim, indices, value) -> Tensor The `index_fill` operator. ## `index_fill_` ```python index_fill_index_fill_(self: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` index_fill_(self: clika_runtime._core.Tensor, dim: int, indices: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor index_fill_(self, dim, indices, value) -> Tensor The `index_fill_` operator. ## `index_put` ```python index_putindex_put(input: clika_runtime._core.Tensor, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry], value: clika_runtime._core.ops.ScalarOrTensor, accumulate: bool = False) -> clika_runtime._core.Tensor ``` index_put(input: clika_runtime._core.Tensor, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry], value: clika_runtime._core.ops.ScalarOrTensor, accumulate: bool = False) -> clika_runtime._core.Tensor index_put(input, indices, value, accumulate=False) -> Tensor The `index_put` operator. ## `index_put_` ```python index_put_index_put_(self: clika_runtime._core.Tensor, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry], value: clika_runtime._core.ops.ScalarOrTensor, accumulate: bool = False) -> clika_runtime._core.Tensor ``` index_put_(self: clika_runtime._core.Tensor, indices: collections.abc.Sequence[clika_runtime._core.ops.IndexEntry], value: clika_runtime._core.ops.ScalarOrTensor, accumulate: bool = False) -> clika_runtime._core.Tensor index_put_(self, indices, value, accumulate=False) -> Tensor The `index_put_` operator. ## `index_select` ```python index_selectindex_select(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` index_select(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor index_select(input, dim, index) -> Tensor The `index_select` operator. ## `inner` ```python innerinner(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` inner(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor inner(input, other) -> Tensor The `inner` operator. ## `instance_norm` ```python instance_norminstance_norm(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, running_mean: clika_runtime._core.Tensor | None = None, running_var: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor ``` instance_norm(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, running_mean: clika_runtime._core.Tensor | None = None, running_var: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor instance_norm(input, weight=None, bias=None, running_mean=None, running_var=None, eps=None, *, activation=None) -> Tensor The `instance_norm` operator. ## `interpolate` ```python interpolateinterpolate(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], mode: object = 'nearest', align_corners: bool | None = None, recompute_scale_factor: bool = False, antialias: bool = False) -> clika_runtime._core.Tensor ``` interpolate(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], mode: object = 'nearest', align_corners: bool | None = None, recompute_scale_factor: bool = False, antialias: bool = False) -> clika_runtime._core.Tensor interpolate(input, sizes=[], scale_factors=[], mode='nearest', align_corners=None, recompute_scale_factor=False, antialias=False) -> Tensor The `interpolate` operator. ## `isclose` ```python iscloseisclose(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> clika_runtime._core.Tensor ``` isclose(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False) -> clika_runtime._core.Tensor isclose(input, other, rtol=1e-5, atol=1e-8, equal_nan=False) -> Tensor The `isclose` operator. ## `isfinite` ```python isfiniteisfinite(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` isfinite(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor isfinite(input) -> Tensor The `isfinite` operator. ## `isinf` ```python isinfisinf(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` isinf(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor isinf(input) -> Tensor The `isinf` operator. ## `isnan` ```python isnanisnan(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` isnan(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor isnan(input) -> Tensor The `isnan` operator. ## `isneginf` ```python isneginfisneginf(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` isneginf(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor isneginf(input) -> Tensor The `isneginf` operator. ## `isposinf` ```python isposinfisposinf(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` isposinf(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor isposinf(input) -> Tensor The `isposinf` operator. ## `kl_div` ```python kl_divkl_div(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean', log_target: bool = False) -> clika_runtime._core.Tensor ``` kl_div(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean', log_target: bool = False) -> clika_runtime._core.Tensor kl_div(input, target, reduction='mean', log_target=False) -> Tensor The `kl_div` operator. ## `kron` ```python kronkron(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` kron(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor kron(input, other) -> Tensor The `kron` operator. ## `kthvalue` ```python kthvaluekthvalue(input: clika_runtime._core.Tensor, k: int, dim: int = -1, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` kthvalue(input: clika_runtime._core.Tensor, k: int, dim: int = -1, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] kthvalue(input, k, dim=-1, keepdim=False) -> tuple[Tensor, Tensor] The `kthvalue` operator. ## `l1_loss` ```python l1_lossl1_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean') -> clika_runtime._core.Tensor ``` l1_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean') -> clika_runtime._core.Tensor l1_loss(input, target, reduction='mean') -> Tensor The `l1_loss` operator. ## `layer_norm` ```python layer_normlayer_norm(input: clika_runtime._core.Tensor, normalized_shape: collections.abc.Sequence[int], weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor ``` layer_norm(input: clika_runtime._core.Tensor, normalized_shape: collections.abc.Sequence[int], weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor layer_norm(input, normalized_shape, weight=None, bias=None, eps=None, *, activation=None) -> Tensor The `layer_norm` operator. ## `le` ```python lele(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` le(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor le(input, other) -> Tensor The `le` operator. ## `le_` ```python le_le_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` le_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor le_(self, other) -> Tensor The `le_` operator. ## `leaky_relu` ```python leaky_reluleaky_relu(input: clika_runtime._core.Tensor, negative_slope: float = 0.01) -> clika_runtime._core.Tensor ``` leaky_relu(input: clika_runtime._core.Tensor, negative_slope: float = 0.01) -> clika_runtime._core.Tensor leaky_relu(input, negative_slope=0.01) -> Tensor The `leaky_relu` operator. ## `leaky_relu_` ```python leaky_relu_leaky_relu_(self: clika_runtime._core.Tensor, negative_slope: float = 0.01) -> clika_runtime._core.Tensor ``` leaky_relu_(self: clika_runtime._core.Tensor, negative_slope: float = 0.01) -> clika_runtime._core.Tensor leaky_relu_(self, negative_slope=0.01) -> Tensor The `leaky_relu_` operator. ## `linear` ```python linearlinear(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, situ_beta: float = 0.0, situ_linear_beta: float = 0.0) -> clika_runtime._core.Tensor ``` linear(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, situ_beta: float = 0.0, situ_linear_beta: float = 0.0) -> clika_runtime._core.Tensor linear(input, weight, bias=None, *, activation=None, situ_beta=0.0, situ_linear_beta=0.0) -> Tensor The `linear` operator. ## `linspace` ```python linspacelinspace(start: clika_runtime._core.ops.ScalarOrTensor, end: clika_runtime._core.ops.ScalarOrTensor, steps: clika_runtime._core.ops.ScalarOrTensor, *, dtype: clika_runtime._core.DataType = DataType.Float32, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` linspace(start: clika_runtime._core.ops.ScalarOrTensor, end: clika_runtime._core.ops.ScalarOrTensor, steps: clika_runtime._core.ops.ScalarOrTensor, *, dtype: clika_runtime._core.DataType = DataType.Float32, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor linspace(start, end, steps, *, dtype=Float32, device=None) -> Tensor The `linspace` operator. ## `log` ```python loglog(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` log(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor log(input) -> Tensor The `log` operator. ## `log10` ```python log10log10(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` log10(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor log10(input) -> Tensor The `log10` operator. ## `log10_` ```python log10_log10_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` log10_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor log10_(self) -> Tensor The `log10_` operator. ## `log1p` ```python log1plog1p(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` log1p(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor log1p(input) -> Tensor The `log1p` operator. ## `log1p_` ```python log1p_log1p_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` log1p_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor log1p_(self) -> Tensor The `log1p_` operator. ## `log2` ```python log2log2(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` log2(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor log2(input) -> Tensor The `log2` operator. ## `log2_` ```python log2_log2_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` log2_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor log2_(self) -> Tensor The `log2_` operator. ## `log_` ```python log_log_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` log_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor log_(self) -> Tensor The `log_` operator. ## `log_sigmoid` ```python log_sigmoidlog_sigmoid(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` log_sigmoid(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor log_sigmoid(input) -> Tensor The `log_sigmoid` operator. ## `log_sigmoid_` ```python log_sigmoid_log_sigmoid_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` log_sigmoid_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor log_sigmoid_(self) -> Tensor The `log_sigmoid_` operator. ## `log_softmax` ```python log_softmaxlog_softmax(input: clika_runtime._core.Tensor, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor ``` log_softmax(input: clika_runtime._core.Tensor, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor log_softmax(input, dim=-1, *, dtype=Undefined) -> Tensor The `log_softmax` operator. ## `log_softmax_` ```python log_softmax_log_softmax_(self: clika_runtime._core.Tensor, dim: int = -1) -> clika_runtime._core.Tensor ``` log_softmax_(self: clika_runtime._core.Tensor, dim: int = -1) -> clika_runtime._core.Tensor log_softmax_(self, dim=-1) -> Tensor The `log_softmax_` operator. ## `logaddexp` ```python logaddexplogaddexp(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` logaddexp(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor logaddexp(input, other) -> Tensor The `logaddexp` operator. ## `logaddexp2` ```python logaddexp2logaddexp2(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` logaddexp2(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor logaddexp2(input, other) -> Tensor The `logaddexp2` operator. ## `logical_and` ```python logical_andlogical_and(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` logical_and(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor logical_and(input, other) -> Tensor The `logical_and` operator. ## `logical_and_` ```python logical_and_logical_and_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` logical_and_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor logical_and_(self, other) -> Tensor The `logical_and_` operator. ## `logical_not` ```python logical_notlogical_not(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` logical_not(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor logical_not(input) -> Tensor The `logical_not` operator. ## `logical_not_` ```python logical_not_logical_not_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` logical_not_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor logical_not_(self) -> Tensor The `logical_not_` operator. ## `logical_or` ```python logical_orlogical_or(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` logical_or(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor logical_or(input, other) -> Tensor The `logical_or` operator. ## `logical_or_` ```python logical_or_logical_or_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` logical_or_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor logical_or_(self, other) -> Tensor The `logical_or_` operator. ## `logical_xor` ```python logical_xorlogical_xor(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` logical_xor(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor logical_xor(input, other) -> Tensor The `logical_xor` operator. ## `logical_xor_` ```python logical_xor_logical_xor_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` logical_xor_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor logical_xor_(self, other) -> Tensor The `logical_xor_` operator. ## `logit` ```python logitlogit(input: clika_runtime._core.Tensor, eps: float | None = None) -> clika_runtime._core.Tensor ``` logit(input: clika_runtime._core.Tensor, eps: float | None = None) -> clika_runtime._core.Tensor logit(input, eps=None) -> Tensor The `logit` operator. ## `logit_` ```python logit_logit_(self: clika_runtime._core.Tensor, eps: float | None = None) -> clika_runtime._core.Tensor ``` logit_(self: clika_runtime._core.Tensor, eps: float | None = None) -> clika_runtime._core.Tensor logit_(self, eps=None) -> Tensor The `logit_` operator. ## `logsumexp` ```python logsumexplogsumexp(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int], keepdim: bool = False) -> clika_runtime._core.Tensor ``` logsumexp(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int], keepdim: bool = False) -> clika_runtime._core.Tensor logsumexp(input, dims, keepdim=False) -> Tensor The `logsumexp` operator. ## `lt` ```python ltlt(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` lt(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor lt(input, other) -> Tensor The `lt` operator. ## `lt_` ```python lt_lt_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` lt_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor lt_(self, other) -> Tensor The `lt_` operator. ## `masked_fill` ```python masked_fillmasked_fill(input: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` masked_fill(input: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor masked_fill(input, mask, value) -> Tensor The `masked_fill` operator. ## `masked_fill_` ```python masked_fill_masked_fill_(self: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` masked_fill_(self: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor, value: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor masked_fill_(self, mask, value) -> Tensor The `masked_fill_` operator. ## `masked_scatter` ```python masked_scattermasked_scatter(input: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` masked_scatter(input: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor masked_scatter(input, mask, source) -> Tensor The `masked_scatter` operator. ## `masked_scatter_` ```python masked_scatter_masked_scatter_(self: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` masked_scatter_(self: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor masked_scatter_(self, mask, source) -> Tensor The `masked_scatter_` operator. ## `masked_select` ```python masked_selectmasked_select(input: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` masked_select(input: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor masked_select(input, mask) -> Tensor The `masked_select` operator. ## `matmul` ```python matmulmatmul(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False, situ_beta: float = 0.0, situ_linear_beta: float = 0.0) -> clika_runtime._core.Tensor ``` matmul(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False, situ_beta: float = 0.0, situ_linear_beta: float = 0.0) -> clika_runtime._core.Tensor matmul(input, other, bias=None, *, activation=None, transpose_a=False, transpose_b=False, situ_beta=0.0, situ_linear_beta=0.0) -> Tensor The `matmul` operator. ## `max` ```python maxmax(input: clika_runtime._core.Tensor, dim: int, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` max(input: clika_runtime._core.Tensor, dim: int, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] max(input, dim, keepdim=False) -> tuple[Tensor, Tensor] The `max` operator. ## `max_pool` ```python max_poolmax_pool(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], ceil_mode: bool) -> clika_runtime._core.Tensor ``` max_pool(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], ceil_mode: bool) -> clika_runtime._core.Tensor max_pool(input, kernel_size, stride, padding, dilation, ceil_mode) -> Tensor The `max_pool` operator. ## `max_pool1d` ```python max_pool1dmax_pool1d(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], ceil_mode: bool = False) -> clika_runtime._core.Tensor ``` max_pool1d(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], ceil_mode: bool = False) -> clika_runtime._core.Tensor max_pool1d(input, kernel_size, stride=[], padding=[0], dilation=[1], ceil_mode=False) -> Tensor The `max_pool1d` operator. ## `max_pool1d_with_indices` ```python max_pool1d_with_indicesmax_pool1d_with_indices(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], ceil_mode: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` max_pool1d_with_indices(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], ceil_mode: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] max_pool1d_with_indices(input, kernel_size, stride=[], padding=[0], dilation=[1], ceil_mode=False) -> tuple[Tensor, Tensor] The `max_pool1d_with_indices` operator. ## `max_pool2d` ```python max_pool2dmax_pool2d(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], ceil_mode: bool = False) -> clika_runtime._core.Tensor ``` max_pool2d(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], ceil_mode: bool = False) -> clika_runtime._core.Tensor max_pool2d(input, kernel_size, stride=[], padding=[0, 0], dilation=[1, 1], ceil_mode=False) -> Tensor The `max_pool2d` operator. ## `max_pool2d_with_indices` ```python max_pool2d_with_indicesmax_pool2d_with_indices(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], ceil_mode: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` max_pool2d_with_indices(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], ceil_mode: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] max_pool2d_with_indices(input, kernel_size, stride=[], padding=[0, 0], dilation=[1, 1], ceil_mode=False) -> tuple[Tensor, Tensor] The `max_pool2d_with_indices` operator. ## `max_pool3d` ```python max_pool3dmax_pool3d(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], ceil_mode: bool = False) -> clika_runtime._core.Tensor ``` max_pool3d(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], ceil_mode: bool = False) -> clika_runtime._core.Tensor max_pool3d(input, kernel_size, stride=[], padding=[0, 0, 0], dilation=[1, 1, 1], ceil_mode=False) -> Tensor The `max_pool3d` operator. ## `max_pool3d_with_indices` ```python max_pool3d_with_indicesmax_pool3d_with_indices(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], ceil_mode: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` max_pool3d_with_indices(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], ceil_mode: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] max_pool3d_with_indices(input, kernel_size, stride=[], padding=[0, 0, 0], dilation=[1, 1, 1], ceil_mode=False) -> tuple[Tensor, Tensor] The `max_pool3d_with_indices` operator. ## `max_pool_with_indices` ```python max_pool_with_indicesmax_pool_with_indices(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], ceil_mode: bool) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` max_pool_with_indices(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], ceil_mode: bool) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] max_pool_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode) -> tuple[Tensor, Tensor] The `max_pool_with_indices` operator. ## `maximum` ```python maximummaximum(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` maximum(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor maximum(input, other) -> Tensor The `maximum` operator. ## `maximum_` ```python maximum_maximum_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` maximum_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor maximum_(self, other) -> Tensor The `maximum_` operator. ## `mean` ```python meanmean(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor ``` mean(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor mean(input, dims=[], keepdim=False, *, dtype=Undefined) -> Tensor The `mean` operator. ## `median` ```python medianmedian(input: clika_runtime._core.Tensor, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` median(input: clika_runtime._core.Tensor, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] median(input, dim=None, keepdim=False) -> tuple[Tensor, Tensor] The `median` operator. ## `meshgrid` ```python meshgridmeshgrid(tensors: collections.abc.Sequence[clika_runtime._core.Tensor], indexing: object = 'ij') -> list[clika_runtime._core.Tensor] ``` meshgrid(tensors: collections.abc.Sequence[clika_runtime._core.Tensor], indexing: object = 'ij') -> list[clika_runtime._core.Tensor] meshgrid(tensors, indexing='ij') -> list[Tensor] The `meshgrid` operator. ## `min` ```python minmin(input: clika_runtime._core.Tensor, dim: int, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` min(input: clika_runtime._core.Tensor, dim: int, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] min(input, dim, keepdim=False) -> tuple[Tensor, Tensor] The `min` operator. ## `minimum` ```python minimumminimum(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` minimum(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor minimum(input, other) -> Tensor The `minimum` operator. ## `minimum_` ```python minimum_minimum_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` minimum_(self: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor minimum_(self, other) -> Tensor The `minimum_` operator. ## `mish` ```python mishmish(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` mish(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor mish(input) -> Tensor The `mish` operator. ## `mish_` ```python mish_mish_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` mish_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor mish_(self) -> Tensor The `mish_` operator. ## `mla_attention` ```python mla_attentionmla_attention(q_nope: clika_runtime._core.Tensor, q_pe: clika_runtime._core.Tensor, new_ckv: clika_runtime._core.Tensor, new_kpe: clika_runtime._core.Tensor, ckv_cache: clika_runtime._core.Tensor, kpe_cache: clika_runtime._core.Tensor, kvcache_start: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, scale: float, slot_ids: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` mla_attention(q_nope: clika_runtime._core.Tensor, q_pe: clika_runtime._core.Tensor, new_ckv: clika_runtime._core.Tensor, new_kpe: clika_runtime._core.Tensor, ckv_cache: clika_runtime._core.Tensor, kpe_cache: clika_runtime._core.Tensor, kvcache_start: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, scale: float, slot_ids: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor mla_attention(q_nope, q_pe, new_ckv, new_kpe, ckv_cache, kpe_cache, kvcache_start, cu_seqlens_q, scale, slot_ids=None) -> Tensor The `mla_attention` operator. ## `mm` ```python mmmm(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor ``` mm(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor mm(input, other, bias=None, *, activation=None) -> Tensor The `mm` operator. ## `mod` ```python modmod(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, mode: object = 'python') -> clika_runtime._core.Tensor ``` mod(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, mode: object = 'python') -> clika_runtime._core.Tensor mod(input, other, mode='python') -> Tensor The `mod` operator. ## `mod_` ```python mod_mod_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, mode: object = 'python') -> clika_runtime._core.Tensor ``` mod_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, mode: object = 'python') -> clika_runtime._core.Tensor mod_(self, other, mode='python') -> Tensor The `mod_` operator. ## `moe` ```python moemoe(input: clika_runtime._core.Tensor, router_logits: clika_runtime._core.Tensor, fc1_experts: clika_runtime._core.Tensor, fc2_experts: clika_runtime._core.Tensor, top_k: int, fc1_bias: clika_runtime._core.Tensor | None = None, fc2_bias: clika_runtime._core.Tensor | None = None, fc3_experts: clika_runtime._core.Tensor | None = None, fc3_bias: clika_runtime._core.Tensor | None = None, e_score_correction_bias: clika_runtime._core.Tensor | None = None, router_weights: clika_runtime._core.Tensor | None = None, routing_mode: object | None = None, renormalize: bool | None = None, n_group: int | None = None, topk_group: int | None = None, routed_scaling_factor: float | None = None, sparse_mixer_eps: float | None = None, apply_router_weight_on_input: bool | None = None, *, activation: object | None = None, swiglu_fusion: object | None = None, swiglu_alpha: float | None = None, swiglu_beta: float | None = None, swiglu_limit: float | None = None, gelu_mode: object | None = None, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` moe(input: clika_runtime._core.Tensor, router_logits: clika_runtime._core.Tensor, fc1_experts: clika_runtime._core.Tensor, fc2_experts: clika_runtime._core.Tensor, top_k: int, fc1_bias: clika_runtime._core.Tensor | None = None, fc2_bias: clika_runtime._core.Tensor | None = None, fc3_experts: clika_runtime._core.Tensor | None = None, fc3_bias: clika_runtime._core.Tensor | None = None, e_score_correction_bias: clika_runtime._core.Tensor | None = None, router_weights: clika_runtime._core.Tensor | None = None, routing_mode: object | None = None, renormalize: bool | None = None, n_group: int | None = None, topk_group: int | None = None, routed_scaling_factor: float | None = None, sparse_mixer_eps: float | None = None, apply_router_weight_on_input: bool | None = None, *, activation: object | None = None, swiglu_fusion: object | None = None, swiglu_alpha: float | None = None, swiglu_beta: float | None = None, swiglu_limit: float | None = None, gelu_mode: object | None = None, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor moe(input, router_logits, fc1_experts, fc2_experts, top_k, fc1_bias=None, fc2_bias=None, fc3_experts=None, fc3_bias=None, e_score_correction_bias=None, router_weights=None, routing_mode=None, renormalize=None, n_group=None, topk_group=None, routed_scaling_factor=None, sparse_mixer_eps=None, apply_router_weight_on_input=None, *, activation=None, swiglu_fusion=None, swiglu_alpha=None, swiglu_beta=None, swiglu_limit=None, gelu_mode=None, shared_output=None) -> Tensor The `moe` operator. ## `mrope_rotary_embedding` ```python mrope_rotary_embeddingmrope_rotary_embedding(input: clika_runtime._core.Tensor, position_ids: clika_runtime._core.Tensor, mrope_sections: collections.abc.Sequence[int], interleaved_sections: bool | None = None, theta: float | None = None, scaling: object | None = None, mode: object | None = None, rotary_dim: int | None = None, scale: float | None = None) -> clika_runtime._core.Tensor ``` mrope_rotary_embedding(input: clika_runtime._core.Tensor, position_ids: clika_runtime._core.Tensor, mrope_sections: collections.abc.Sequence[int], interleaved_sections: bool | None = None, theta: float | None = None, scaling: object | None = None, mode: object | None = None, rotary_dim: int | None = None, scale: float | None = None) -> clika_runtime._core.Tensor mrope_rotary_embedding(input, position_ids, mrope_sections, interleaved_sections=None, theta=None, scaling=None, mode=None, rotary_dim=None, scale=None) -> Tensor The `mrope_rotary_embedding` operator. ## `mrope_rotary_embedding_qk_varlen` ```python mrope_rotary_embedding_qk_varlenmrope_rotary_embedding_qk_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, position_ids: clika_runtime._core.Tensor, mrope_sections: collections.abc.Sequence[int], interleaved_sections: bool | None = None, theta: float | None = None, scaling: object | None = None, mode: object | None = None, rotary_dim: int | None = None, scale: float | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` mrope_rotary_embedding_qk_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, position_ids: clika_runtime._core.Tensor, mrope_sections: collections.abc.Sequence[int], interleaved_sections: bool | None = None, theta: float | None = None, scaling: object | None = None, mode: object | None = None, rotary_dim: int | None = None, scale: float | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] mrope_rotary_embedding_qk_varlen(query, key, position_ids, mrope_sections, interleaved_sections=None, theta=None, scaling=None, mode=None, rotary_dim=None, scale=None) -> tuple[Tensor, Tensor] The `mrope_rotary_embedding_qk_varlen` operator. ## `mrope_rotary_embedding_varlen` ```python mrope_rotary_embedding_varlenmrope_rotary_embedding_varlen(input: clika_runtime._core.Tensor, position_ids: clika_runtime._core.Tensor, mrope_sections: collections.abc.Sequence[int], interleaved_sections: bool | None = None, theta: float | None = None, scaling: object | None = None, mode: object | None = None, rotary_dim: int | None = None, scale: float | None = None) -> clika_runtime._core.Tensor ``` mrope_rotary_embedding_varlen(input: clika_runtime._core.Tensor, position_ids: clika_runtime._core.Tensor, mrope_sections: collections.abc.Sequence[int], interleaved_sections: bool | None = None, theta: float | None = None, scaling: object | None = None, mode: object | None = None, rotary_dim: int | None = None, scale: float | None = None) -> clika_runtime._core.Tensor mrope_rotary_embedding_varlen(input, position_ids, mrope_sections, interleaved_sections=None, theta=None, scaling=None, mode=None, rotary_dim=None, scale=None) -> Tensor The `mrope_rotary_embedding_varlen` operator. ## `ms_deform_attention` ```python ms_deform_attentionms_deform_attention(value: clika_runtime._core.Tensor, spatial_shapes: clika_runtime._core.Tensor, level_start_index: clika_runtime._core.Tensor, sampling_locations: clika_runtime._core.Tensor, attention_weights: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` ms_deform_attention(value: clika_runtime._core.Tensor, spatial_shapes: clika_runtime._core.Tensor, level_start_index: clika_runtime._core.Tensor, sampling_locations: clika_runtime._core.Tensor, attention_weights: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ms_deform_attention(value, spatial_shapes, level_start_index, sampling_locations, attention_weights) -> Tensor The `ms_deform_attention` operator. ## `mse_loss` ```python mse_lossmse_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean') -> clika_runtime._core.Tensor ``` mse_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean') -> clika_runtime._core.Tensor mse_loss(input, target, reduction='mean') -> Tensor The `mse_loss` operator. ## `mul` ```python mulmul(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` mul(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor mul(input: clika_runtime._core.ops.Scalar, other: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor mul(input, other, *, activation='identity') -> Tensor The `mul` operator. ## `mul_` ```python mul_mul_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` mul_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor mul_(self, other, *, activation='identity') -> Tensor The `mul_` operator. ## `multinomial` ```python multinomialmultinomial(probabilities: clika_runtime._core.Tensor, num_samples: int, replacement: bool = False, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` multinomial(probabilities: clika_runtime._core.Tensor, num_samples: int, replacement: bool = False, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor multinomial(probabilities, num_samples, replacement=False, *, device=None) -> Tensor The `multinomial` operator. ## `mv` ```python mvmv(input: clika_runtime._core.Tensor, vec: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor ``` mv(input: clika_runtime._core.Tensor, vec: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor mv(input, vec, bias=None, *, activation=None) -> Tensor The `mv` operator. ## `nan_to_num` ```python nan_to_numnan_to_num(input: clika_runtime._core.Tensor, nan: float | None = None, posinf: float | None = None, neginf: float | None = None) -> clika_runtime._core.Tensor ``` nan_to_num(input: clika_runtime._core.Tensor, nan: float | None = None, posinf: float | None = None, neginf: float | None = None) -> clika_runtime._core.Tensor nan_to_num(input, nan=None, posinf=None, neginf=None) -> Tensor The `nan_to_num` operator. ## `nan_to_num_` ```python nan_to_num_nan_to_num_(self: clika_runtime._core.Tensor, nan: float | None = None, posinf: float | None = None, neginf: float | None = None) -> clika_runtime._core.Tensor ``` nan_to_num_(self: clika_runtime._core.Tensor, nan: float | None = None, posinf: float | None = None, neginf: float | None = None) -> clika_runtime._core.Tensor nan_to_num_(self, nan=None, posinf=None, neginf=None) -> Tensor The `nan_to_num_` operator. ## `nanmean` ```python nanmeannanmean(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor ``` nanmean(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor nanmean(input, dims=[], keepdim=False) -> Tensor The `nanmean` operator. ## `nanmedian` ```python nanmediannanmedian(input: clika_runtime._core.Tensor, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` nanmedian(input: clika_runtime._core.Tensor, dim: int | None = None, keepdim: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] nanmedian(input, dim=None, keepdim=False) -> tuple[Tensor, Tensor] The `nanmedian` operator. ## `nanquantile` ```python nanquantilenanquantile(input: clika_runtime._core.Tensor, q: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, keepdim: bool = False, interpolation: object = 'linear') -> clika_runtime._core.Tensor ``` nanquantile(input: clika_runtime._core.Tensor, q: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, keepdim: bool = False, interpolation: object = 'linear') -> clika_runtime._core.Tensor nanquantile(input, q, dim=None, keepdim=False, interpolation='linear') -> Tensor The `nanquantile` operator. ## `nansum` ```python nansumnansum(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor ``` nansum(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor nansum(input, dims=[], keepdim=False) -> Tensor The `nansum` operator. ## `narrow` ```python narrownarrow(input: clika_runtime._core.Tensor, dim: int, start: clika_runtime._core.ops.IndexBound, length: clika_runtime._core.ops.IndexBound) -> clika_runtime._core.Tensor ``` narrow(input: clika_runtime._core.Tensor, dim: int, start: clika_runtime._core.ops.IndexBound, length: clika_runtime._core.ops.IndexBound) -> clika_runtime._core.Tensor narrow(input, dim, start, length) -> Tensor The `narrow` operator. ## `ndim` ```python ndimndim(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` ndim(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ndim(input) -> Tensor The `ndim` operator. ## `ndim_host` ```python ndim_hostndim_host(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` ndim_host(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ndim_host(input) -> Tensor The `ndim_host` operator. ## `ne` ```python nene(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` ne(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ne(input, other) -> Tensor The `ne` operator. ## `ne_` ```python ne_ne_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` ne_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ne_(self, other) -> Tensor The `ne_` operator. ## `neg` ```python negneg(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` neg(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor neg(input) -> Tensor The `neg` operator. ## `neg_` ```python neg_neg_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` neg_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor neg_(self) -> Tensor The `neg_` operator. ## `nll_loss` ```python nll_lossnll_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, ignore_index: int | None = None, reduction: object = 'mean') -> clika_runtime._core.Tensor ``` nll_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None, ignore_index: int | None = None, reduction: object = 'mean') -> clika_runtime._core.Tensor nll_loss(input, target, weight=None, ignore_index=None, reduction='mean') -> Tensor The `nll_loss` operator. ## `nms` ```python nmsnms(boxes: clika_runtime._core.Tensor, scores: clika_runtime._core.Tensor, max_output_boxes_per_class: clika_runtime._core.ops.ScalarOrTensor | None = None, iou_threshold: clika_runtime._core.ops.ScalarOrTensor | None = None, score_threshold: clika_runtime._core.ops.ScalarOrTensor | None = None, center_point_box: bool = False) -> clika_runtime._core.Tensor ``` nms(boxes: clika_runtime._core.Tensor, scores: clika_runtime._core.Tensor, max_output_boxes_per_class: clika_runtime._core.ops.ScalarOrTensor | None = None, iou_threshold: clika_runtime._core.ops.ScalarOrTensor | None = None, score_threshold: clika_runtime._core.ops.ScalarOrTensor | None = None, center_point_box: bool = False) -> clika_runtime._core.Tensor nms(boxes, scores, max_output_boxes_per_class=None, iou_threshold=None, score_threshold=None, center_point_box=False) -> Tensor The `nms` operator. ## `nonzero` ```python nonzerononzero(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` nonzero(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor nonzero(input) -> Tensor The `nonzero` operator. ## `norm` ```python normnorm(input: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...), dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor ``` norm(input: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...), dims: collections.abc.Sequence[int] = [], keepdim: bool = False) -> clika_runtime._core.Tensor norm(input, p=2.0, dims=[], keepdim=False) -> Tensor The `norm` operator. ## `normal` ```python normalnormal(mean: clika_runtime._core.ops.ScalarOrTensor, stddev: clika_runtime._core.ops.ScalarOrTensor, shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` normal(mean: clika_runtime._core.ops.ScalarOrTensor, stddev: clika_runtime._core.ops.ScalarOrTensor, shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor normal(mean, stddev, shape, *, device=None) -> Tensor The `normal` operator. ## `normal_` ```python normal_normal_(self: clika_runtime._core.Tensor, mean: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), stddev: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` normal_(self: clika_runtime._core.Tensor, mean: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), stddev: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor normal_(self, mean=0, stddev=1, *, device=None) -> Tensor The `normal_` operator. ## `normalize` ```python normalizenormalize(input: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...), dim: int = 1, eps: float | None = None) -> clika_runtime._core.Tensor ``` normalize(input: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...), dim: int = 1, eps: float | None = None) -> clika_runtime._core.Tensor normalize(input, p=2.0, dim=1, eps=None) -> Tensor The `normalize` operator. ## `normalize_` ```python normalize_normalize_(self: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...), dim: int = 1, eps: float | None = None) -> clika_runtime._core.Tensor ``` normalize_(self: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...), dim: int = 1, eps: float | None = None) -> clika_runtime._core.Tensor normalize_(self, p=2.0, dim=1, eps=None) -> Tensor The `normalize_` operator. ## `numel` ```python numelnumel(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` numel(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor numel(input) -> Tensor The `numel` operator. ## `numel_host` ```python numel_hostnumel_host(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` numel_host(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor numel_host(input) -> Tensor The `numel_host` operator. ## `one_hot` ```python one_hotone_hot(indices: clika_runtime._core.Tensor, num_classes: int) -> clika_runtime._core.Tensor ``` one_hot(indices: clika_runtime._core.Tensor, num_classes: int) -> clika_runtime._core.Tensor one_hot(indices, num_classes) -> Tensor The `one_hot` operator. ## `ones` ```python onesones(shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], dtype: clika_runtime._core.DataType, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...), pinned_for: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` ones(shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], dtype: clika_runtime._core.DataType, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...), pinned_for: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ones(shape, dtype, *, device=None, pinned_for=None) -> Tensor The `ones` operator. ## `ones_like` ```python ones_likeones_like(reference: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` ones_like(reference: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ones_like(reference, *, device=None) -> Tensor The `ones_like` operator. ## `outer` ```python outerouter(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` outer(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor outer(input, other) -> Tensor The `outer` operator. ## `pad` ```python padpad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], mode: object = 'constant', value: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor ``` pad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], mode: object = 'constant', value: clika_runtime._core.ops.ScalarOrTensor | None = None) -> clika_runtime._core.Tensor pad(input, pad, mode='constant', value=None) -> Tensor The `pad` operator. ## `pairwise_distance` ```python pairwise_distancepairwise_distance(x1: clika_runtime._core.Tensor, x2: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...), eps: float | None = None, keepdim: bool = False) -> clika_runtime._core.Tensor ``` pairwise_distance(x1: clika_runtime._core.Tensor, x2: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...), eps: float | None = None, keepdim: bool = False) -> clika_runtime._core.Tensor pairwise_distance(x1, x2, p=2.0, eps=None, keepdim=False) -> Tensor The `pairwise_distance` operator. ## `pdist` ```python pdistpdist(input: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...)) -> clika_runtime._core.Tensor ``` pdist(input: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar = Scalar(...)) -> clika_runtime._core.Tensor pdist(input, p=2.0) -> Tensor The `pdist` operator. ## `permute` ```python permutepermute(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor ``` permute(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor permute(input, dims) -> Tensor The `permute` operator. ## `pixel_shuffle` ```python pixel_shufflepixel_shuffle(input: clika_runtime._core.Tensor, upscale_factor: int, mode: object = 'crd') -> clika_runtime._core.Tensor ``` pixel_shuffle(input: clika_runtime._core.Tensor, upscale_factor: int, mode: object = 'crd') -> clika_runtime._core.Tensor pixel_shuffle(input, upscale_factor, mode='crd') -> Tensor The `pixel_shuffle` operator. ## `pixel_unshuffle` ```python pixel_unshufflepixel_unshuffle(input: clika_runtime._core.Tensor, downscale_factor: int, mode: object = 'crd') -> clika_runtime._core.Tensor ``` pixel_unshuffle(input: clika_runtime._core.Tensor, downscale_factor: int, mode: object = 'crd') -> clika_runtime._core.Tensor pixel_unshuffle(input, downscale_factor, mode='crd') -> Tensor The `pixel_unshuffle` operator. ## `poisson` ```python poissonpoisson(rates: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` poisson(rates: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor poisson(rates, *, device=None) -> Tensor The `poisson` operator. ## `pow` ```python powpow(input: clika_runtime._core.Tensor, exponent: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` pow(input: clika_runtime._core.Tensor, exponent: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor pow(input, exponent) -> Tensor The `pow` operator. ## `pow_` ```python pow_pow_(self: clika_runtime._core.Tensor, exponent: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` pow_(self: clika_runtime._core.Tensor, exponent: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor pow_(self, exponent) -> Tensor The `pow_` operator. ## `prelu` ```python preluprelu(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` prelu(input: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor prelu(input, weight=None) -> Tensor The `prelu` operator. ## `prelu_` ```python prelu_prelu_(self: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` prelu_(self: clika_runtime._core.Tensor, weight: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor prelu_(self, weight=None) -> Tensor The `prelu_` operator. ## `prod` ```python prodprod(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor ``` prod(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor prod(input, dims=[], keepdim=False, *, dtype=Undefined) -> Tensor The `prod` operator. ## `put` ```python putput(input: clika_runtime._core.Tensor, index: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor, accumulate: bool = False) -> clika_runtime._core.Tensor ``` put(input: clika_runtime._core.Tensor, index: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor, accumulate: bool = False) -> clika_runtime._core.Tensor put(input, index, source, accumulate=False) -> Tensor The `put` operator. ## `put_` ```python put_put_(self: clika_runtime._core.Tensor, index: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor, accumulate: bool = False) -> clika_runtime._core.Tensor ``` put_(self: clika_runtime._core.Tensor, index: clika_runtime._core.Tensor, source: clika_runtime._core.Tensor, accumulate: bool = False) -> clika_runtime._core.Tensor put_(self, index, source, accumulate=False) -> Tensor The `put_` operator. ## `q_embedding` ```python q_embeddingq_embedding(indices: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor ``` q_embedding(indices: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor q_embedding(indices, weight, bias=None, *, activation=None, out_dtype=Undefined) -> Tensor The `q_embedding` operator. ## `qadd` ```python qaddqadd(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qadd(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qadd(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor Overloaded function. 1. ``qadd(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qadd(input, other, *, activation='identity') -> Tensor The `qadd` operator. 2. ``qadd(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor`` qadd(input, other, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined, activation='identity') -> QTensor The `qadd` operator. ## `qadd_` ```python qadd_qadd_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor ``` qadd_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor qadd_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qadd_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor Overloaded function. 1. ``qadd_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor`` qadd_(input, other, out, *, activation='identity') -> QTensor The `qadd_` operator. 2. ``qadd_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qadd_(input, other, out, *, activation='identity') -> Tensor The `qadd_` operator. 3. ``qadd_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qadd_(input, other, out, out_scale, out_zero_point=None, out_quant_axis=-1, *, activation='identity') -> Tensor The `qadd_` operator. ## `qconcat` ```python qconcatqconcat(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int = 0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconcat(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int = 0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconcat(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor Overloaded function. 1. ``qconcat(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int = 0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconcat(tensors, dim=0, *, activation='identity') -> Tensor The `qconcat` operator. 2. ``qconcat(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor`` qconcat(tensors, dim, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined, activation='identity') -> QTensor The `qconcat` operator. ## `qconcat_` ```python qconcat_qconcat_(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor ``` qconcat_(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor qconcat_(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor Overloaded function. 1. ``qconcat_(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor`` qconcat_(tensors, dim, out, *, activation='identity') -> QTensor The `qconcat_` operator. 2. ``qconcat_(tensors: collections.abc.Sequence[clika_runtime._core.QTensor], dim: int, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconcat_(tensors, dim, out, out_scale, out_zero_point=None, out_quant_axis=-1, *, activation='identity') -> Tensor The `qconcat_` operator. ## `qconv` ```python qconvqconv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, mode: object, value: float | None, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qconv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconv(input, weight, bias=None, stride=[], padding=[], dilation=[], groups=1, mode='constant', value=None, *, activation='identity') -> Tensor The `qconv` operator. 2. ``qconv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, mode: object, value: float | None, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qconv(input, weight, bias, stride, padding, dilation, groups, mode, value, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qconv` operator. ## `qconv1d` ```python qconv1dqconv1d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv1d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv1d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qconv1d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconv1d(input, weight, bias=None, stride=[1], padding=[0], dilation=[1], groups=1, *, activation='identity') -> Tensor The `qconv1d` operator. 2. ``qconv1d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qconv1d(input, weight, bias, stride, padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qconv1d` operator. ## `qconv1d_woq` ```python qconv1d_woqqconv1d_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv1d_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv1d_woq(input, weight, bias=None, stride=[1], padding=[0], dilation=[1], groups=1, *, activation='identity') -> Tensor The `qconv1d_woq` operator. ## `qconv2d` ```python qconv2dqconv2d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv2d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv2d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qconv2d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconv2d(input, weight, bias=None, stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, *, activation='identity') -> Tensor The `qconv2d` operator. 2. ``qconv2d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qconv2d(input, weight, bias, stride, padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qconv2d` operator. ## `qconv2d_woq` ```python qconv2d_woqqconv2d_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv2d_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv2d_woq(input, weight, bias=None, stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, *, activation='identity') -> Tensor The `qconv2d_woq` operator. ## `qconv3d` ```python qconv3dqconv3d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv3d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv3d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qconv3d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconv3d(input, weight, bias=None, stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], groups=1, *, activation='identity') -> Tensor The `qconv3d` operator. 2. ``qconv3d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qconv3d(input, weight, bias, stride, padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qconv3d` operator. ## `qconv3d_woq` ```python qconv3d_woqqconv3d_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv3d_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv3d_woq(input, weight, bias=None, stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], groups=1, *, activation='identity') -> Tensor The `qconv3d_woq` operator. ## `qconv_` ```python qconv_qconv_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor ``` qconv_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor qconv_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor Overloaded function. 1. ``qconv_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor`` qconv_(input, weight, out, bias=None, stride=[], padding=[], dilation=[], groups=1, mode='constant', value=None, *, activation='identity') -> QTensor The `qconv_` operator. 2. ``qconv_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconv_(input, weight, out, bias=None, stride=[], padding=[], dilation=[], groups=1, mode='constant', value=None, *, activation='identity') -> Tensor The `qconv_` operator. 3. ``qconv_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconv_(input, weight, out, out_scale, out_zero_point, out_quant_axis=-1, bias=None, stride=[], padding=[], dilation=[], groups=1, mode='constant', value=None, *, activation='identity') -> Tensor The `qconv_` operator. ## `qconv_transpose` ```python qconv_transposeqconv_transpose(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv_transpose(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv_transpose(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qconv_transpose(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconv_transpose(input, weight, bias=None, stride=[], padding=[], output_padding=[], dilation=[], groups=1, *, activation='identity') -> Tensor The `qconv_transpose` operator. 2. ``qconv_transpose(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qconv_transpose(input, weight, bias, stride, padding, output_padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qconv_transpose` operator. ## `qconv_transpose1d` ```python qconv_transpose1dqconv_transpose1d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], output_padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv_transpose1d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], output_padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv_transpose1d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qconv_transpose1d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], output_padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconv_transpose1d(input, weight, bias=None, stride=[1], padding=[0], output_padding=[0], dilation=[1], groups=1, *, activation='identity') -> Tensor The `qconv_transpose1d` operator. 2. ``qconv_transpose1d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qconv_transpose1d(input, weight, bias, stride, padding, output_padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qconv_transpose1d` operator. ## `qconv_transpose1d_woq` ```python qconv_transpose1d_woqqconv_transpose1d_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], output_padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv_transpose1d_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1], padding: collections.abc.Sequence[int] = [0], output_padding: collections.abc.Sequence[int] = [0], dilation: collections.abc.Sequence[int] = [1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv_transpose1d_woq(input, weight, bias=None, stride=[1], padding=[0], output_padding=[0], dilation=[1], groups=1, *, activation='identity') -> Tensor The `qconv_transpose1d_woq` operator. ## `qconv_transpose2d` ```python qconv_transpose2dqconv_transpose2d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], output_padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv_transpose2d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], output_padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv_transpose2d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qconv_transpose2d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], output_padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconv_transpose2d(input, weight, bias=None, stride=[1, 1], padding=[0, 0], output_padding=[0, 0], dilation=[1, 1], groups=1, *, activation='identity') -> Tensor The `qconv_transpose2d` operator. 2. ``qconv_transpose2d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qconv_transpose2d(input, weight, bias, stride, padding, output_padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qconv_transpose2d` operator. ## `qconv_transpose2d_woq` ```python qconv_transpose2d_woqqconv_transpose2d_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], output_padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv_transpose2d_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1], padding: collections.abc.Sequence[int] = [0, 0], output_padding: collections.abc.Sequence[int] = [0, 0], dilation: collections.abc.Sequence[int] = [1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv_transpose2d_woq(input, weight, bias=None, stride=[1, 1], padding=[0, 0], output_padding=[0, 0], dilation=[1, 1], groups=1, *, activation='identity') -> Tensor The `qconv_transpose2d_woq` operator. ## `qconv_transpose3d` ```python qconv_transpose3dqconv_transpose3d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv_transpose3d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv_transpose3d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qconv_transpose3d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconv_transpose3d(input, weight, bias=None, stride=[1, 1, 1], padding=[0, 0, 0], output_padding=[0, 0, 0], dilation=[1, 1, 1], groups=1, *, activation='identity') -> Tensor The `qconv_transpose3d` operator. 2. ``qconv_transpose3d(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], output_padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qconv_transpose3d(input, weight, bias, stride, padding, output_padding, dilation, groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qconv_transpose3d` operator. ## `qconv_transpose3d_woq` ```python qconv_transpose3d_woqqconv_transpose3d_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qconv_transpose3d_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [1, 1, 1], padding: collections.abc.Sequence[int] = [0, 0, 0], output_padding: collections.abc.Sequence[int] = [0, 0, 0], dilation: collections.abc.Sequence[int] = [1, 1, 1], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv_transpose3d_woq(input, weight, bias=None, stride=[1, 1, 1], padding=[0, 0, 0], output_padding=[0, 0, 0], dilation=[1, 1, 1], groups=1, *, activation='identity') -> Tensor The `qconv_transpose3d_woq` operator. ## `qconv_transpose_` ```python qconv_transpose_qconv_transpose_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor ``` qconv_transpose_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor qconv_transpose_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qconv_transpose_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor Overloaded function. 1. ``qconv_transpose_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor`` qconv_transpose_(input, weight, out, bias=None, stride=[], padding=[], output_padding=[], dilation=[], groups=1, *, activation='identity') -> QTensor The `qconv_transpose_` operator. 2. ``qconv_transpose_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconv_transpose_(input, weight, out, bias=None, stride=[], padding=[], output_padding=[], dilation=[], groups=1, *, activation='identity') -> Tensor The `qconv_transpose_` operator. 3. ``qconv_transpose_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qconv_transpose_(input, weight, out, out_scale, out_zero_point, out_quant_axis=-1, bias=None, stride=[], padding=[], output_padding=[], dilation=[], groups=1, *, activation='identity') -> Tensor The `qconv_transpose_` operator. ## `qconv_transpose_woq` ```python qconv_transpose_woqqconv_transpose_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity', compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor ``` qconv_transpose_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], output_padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, *, activation: object | None = 'identity', compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor qconv_transpose_woq(input, weight, bias=None, stride=[], padding=[], output_padding=[], dilation=[], groups=1, *, activation='identity', compute_mode='exact_fp') -> Tensor The `qconv_transpose_woq` operator. ## `qconv_woq` ```python qconv_woqqconv_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity', compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor ``` qconv_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, mode: object = 'constant', value: float | None = None, *, activation: object | None = 'identity', compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor qconv_woq(input, weight, bias=None, stride=[], padding=[], dilation=[], groups=1, mode='constant', value=None, *, activation='identity', compute_mode='exact_fp') -> Tensor The `qconv_woq` operator. ## `qdeform_conv` ```python qdeform_convqdeform_conv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, offset_groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qdeform_conv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, offset_groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qdeform_conv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, offset_groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qdeform_conv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, offset_groups: int = 1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qdeform_conv(input, weight, offset, mask=None, bias=None, stride=[], padding=[], dilation=[], groups=1, offset_groups=1, *, activation='identity') -> Tensor The `qdeform_conv` operator. 2. ``qdeform_conv(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None, bias: clika_runtime._core.Tensor | None, stride: collections.abc.Sequence[int], padding: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int], groups: int, offset_groups: int, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qdeform_conv(input, weight, offset, mask, bias, stride, padding, dilation, groups, offset_groups, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qdeform_conv` operator. ## `qdeform_conv_woq` ```python qdeform_conv_woqqdeform_conv_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, offset_groups: int = 1, *, activation: object | None = 'identity', compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor ``` qdeform_conv_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, offset: clika_runtime._core.Tensor, mask: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, stride: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], dilation: collections.abc.Sequence[int] = [], groups: int = 1, offset_groups: int = 1, *, activation: object | None = 'identity', compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor qdeform_conv_woq(input, weight, offset, mask=None, bias=None, stride=[], padding=[], dilation=[], groups=1, offset_groups=1, *, activation='identity', compute_mode='exact_fp') -> Tensor The `qdeform_conv_woq` operator. ## `qdiv` ```python qdivqdiv(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qdiv(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qdiv(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor Overloaded function. 1. ``qdiv(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qdiv(input, other, *, activation='identity') -> Tensor The `qdiv` operator. 2. ``qdiv(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor`` qdiv(input, other, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined, activation='identity') -> QTensor The `qdiv` operator. ## `qdiv_` ```python qdiv_qdiv_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor ``` qdiv_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor qdiv_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qdiv_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor Overloaded function. 1. ``qdiv_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor`` qdiv_(input, other, out, *, activation='identity') -> QTensor The `qdiv_` operator. 2. ``qdiv_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qdiv_(input, other, out, *, activation='identity') -> Tensor The `qdiv_` operator. 3. ``qdiv_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qdiv_(input, other, out, out_scale, out_zero_point=None, out_quant_axis=-1, *, activation='identity') -> Tensor The `qdiv_` operator. ## `qfast_gelu` ```python qfast_geluqfast_gelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor ``` qfast_gelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qfast_gelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qfast_gelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor`` qfast_gelu(input) -> Tensor The `qfast_gelu` operator. 2. ``qfast_gelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qfast_gelu(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qfast_gelu` operator. ## `qfast_gelu_` ```python qfast_gelu_qfast_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor ``` qfast_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor qfast_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor qfast_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor Overloaded function. 1. ``qfast_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor`` qfast_gelu_(input, out) -> QTensor The `qfast_gelu_` operator. 2. ``qfast_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` qfast_gelu_(input, out) -> Tensor The `qfast_gelu_` operator. 3. ``qfast_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor`` qfast_gelu_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor The `qfast_gelu_` operator. ## `qgelu` ```python qgeluqgelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor ``` qgelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qgelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qgelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor`` qgelu(input) -> Tensor The `qgelu` operator. 2. ``qgelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qgelu(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qgelu` operator. ## `qgelu_` ```python qgelu_qgelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor ``` qgelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor qgelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor qgelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor Overloaded function. 1. ``qgelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor`` qgelu_(input, out) -> QTensor The `qgelu_` operator. 2. ``qgelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` qgelu_(input, out) -> Tensor The `qgelu_` operator. 3. ``qgelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor`` qgelu_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor The `qgelu_` operator. ## `qhardsigmoid` ```python qhardsigmoidqhardsigmoid(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor ``` qhardsigmoid(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qhardsigmoid(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qhardsigmoid(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor`` qhardsigmoid(input) -> Tensor The `qhardsigmoid` operator. 2. ``qhardsigmoid(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qhardsigmoid(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qhardsigmoid` operator. ## `qhardsigmoid_` ```python qhardsigmoid_qhardsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor ``` qhardsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor qhardsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor qhardsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor Overloaded function. 1. ``qhardsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor`` qhardsigmoid_(input, out) -> QTensor The `qhardsigmoid_` operator. 2. ``qhardsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` qhardsigmoid_(input, out) -> Tensor The `qhardsigmoid_` operator. 3. ``qhardsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor`` qhardsigmoid_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor The `qhardsigmoid_` operator. ## `qhardswish` ```python qhardswishqhardswish(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor ``` qhardswish(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qhardswish(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qhardswish(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor`` qhardswish(input) -> Tensor The `qhardswish` operator. 2. ``qhardswish(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qhardswish(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qhardswish` operator. ## `qhardswish_` ```python qhardswish_qhardswish_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor ``` qhardswish_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor qhardswish_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor qhardswish_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor Overloaded function. 1. ``qhardswish_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor`` qhardswish_(input, out) -> QTensor The `qhardswish_` operator. 2. ``qhardswish_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` qhardswish_(input, out) -> Tensor The `qhardswish_` operator. 3. ``qhardswish_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor`` qhardswish_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor The `qhardswish_` operator. ## `qk_layer_norm` ```python qk_layer_normqk_layer_norm(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor | None = None, value: clika_runtime._core.Tensor | None = None, query_weight: clika_runtime._core.Tensor | None = None, query_bias: clika_runtime._core.Tensor | None = None, key_weight: clika_runtime._core.Tensor | None = None, key_bias: clika_runtime._core.Tensor | None = None, head_dim: int = 0, eps: float | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` qk_layer_norm(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor | None = None, value: clika_runtime._core.Tensor | None = None, query_weight: clika_runtime._core.Tensor | None = None, query_bias: clika_runtime._core.Tensor | None = None, key_weight: clika_runtime._core.Tensor | None = None, key_bias: clika_runtime._core.Tensor | None = None, head_dim: int = 0, eps: float | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] qk_layer_norm(query, key=None, value=None, query_weight=None, query_bias=None, key_weight=None, key_bias=None, head_dim=0, eps=None) -> tuple[Tensor, Tensor, Tensor] The `qk_layer_norm` operator. ## `qk_layer_norm_` ```python qk_layer_norm_qk_layer_norm_(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor | None = None, value: clika_runtime._core.Tensor | None = None, query_weight: clika_runtime._core.Tensor | None = None, query_bias: clika_runtime._core.Tensor | None = None, key_weight: clika_runtime._core.Tensor | None = None, key_bias: clika_runtime._core.Tensor | None = None, head_dim: int = 0, eps: float | None = None) -> clika_runtime._core.Tensor ``` qk_layer_norm_(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor | None = None, value: clika_runtime._core.Tensor | None = None, query_weight: clika_runtime._core.Tensor | None = None, query_bias: clika_runtime._core.Tensor | None = None, key_weight: clika_runtime._core.Tensor | None = None, key_bias: clika_runtime._core.Tensor | None = None, head_dim: int = 0, eps: float | None = None) -> clika_runtime._core.Tensor qk_layer_norm_(query, key=None, value=None, query_weight=None, query_bias=None, key_weight=None, key_bias=None, head_dim=0, eps=None) -> Tensor The `qk_layer_norm_` operator. ## `qk_rms_norm` ```python qk_rms_normqk_rms_norm(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor | None = None, value: clika_runtime._core.Tensor | None = None, query_weight: clika_runtime._core.Tensor | None = None, key_weight: clika_runtime._core.Tensor | None = None, head_dim: int = 0, eps: float | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` qk_rms_norm(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor | None = None, value: clika_runtime._core.Tensor | None = None, query_weight: clika_runtime._core.Tensor | None = None, key_weight: clika_runtime._core.Tensor | None = None, head_dim: int = 0, eps: float | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] qk_rms_norm(query, key=None, value=None, query_weight=None, key_weight=None, head_dim=0, eps=None) -> tuple[Tensor, Tensor, Tensor] The `qk_rms_norm` operator. ## `qk_rms_norm_` ```python qk_rms_norm_qk_rms_norm_(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor | None = None, value: clika_runtime._core.Tensor | None = None, query_weight: clika_runtime._core.Tensor | None = None, key_weight: clika_runtime._core.Tensor | None = None, head_dim: int = 0, eps: float | None = None) -> clika_runtime._core.Tensor ``` qk_rms_norm_(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor | None = None, value: clika_runtime._core.Tensor | None = None, query_weight: clika_runtime._core.Tensor | None = None, key_weight: clika_runtime._core.Tensor | None = None, head_dim: int = 0, eps: float | None = None) -> clika_runtime._core.Tensor qk_rms_norm_(query, key=None, value=None, query_weight=None, key_weight=None, head_dim=0, eps=None) -> Tensor The `qk_rms_norm_` operator. ## `qleaky_relu` ```python qleaky_reluqleaky_relu(input: clika_runtime._core.QTensor, negative_slope: float = 0.01) -> clika_runtime._core.Tensor ``` qleaky_relu(input: clika_runtime._core.QTensor, negative_slope: float = 0.01) -> clika_runtime._core.Tensor qleaky_relu(input: clika_runtime._core.QTensor, negative_slope: float, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qleaky_relu(input: clika_runtime._core.QTensor, negative_slope: float = 0.01) -> clika_runtime._core.Tensor`` qleaky_relu(input, negative_slope=0.01) -> Tensor The `qleaky_relu` operator. 2. ``qleaky_relu(input: clika_runtime._core.QTensor, negative_slope: float, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qleaky_relu(input, negative_slope, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qleaky_relu` operator. ## `qleaky_relu_` ```python qleaky_relu_qleaky_relu_(input: clika_runtime._core.QTensor, negative_slope: float, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor ``` qleaky_relu_(input: clika_runtime._core.QTensor, negative_slope: float, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor qleaky_relu_(input: clika_runtime._core.QTensor, negative_slope: float, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor qleaky_relu_(input: clika_runtime._core.QTensor, negative_slope: float, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor Overloaded function. 1. ``qleaky_relu_(input: clika_runtime._core.QTensor, negative_slope: float, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor`` qleaky_relu_(input, negative_slope, out) -> QTensor The `qleaky_relu_` operator. 2. ``qleaky_relu_(input: clika_runtime._core.QTensor, negative_slope: float, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` qleaky_relu_(input, negative_slope, out) -> Tensor The `qleaky_relu_` operator. 3. ``qleaky_relu_(input: clika_runtime._core.QTensor, negative_slope: float, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor`` qleaky_relu_(input, negative_slope, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor The `qleaky_relu_` operator. ## `qlinear` ```python qlinearqlinear(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor ``` qlinear(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor qlinear(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qlinear(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor`` qlinear(input, weight, bias=None, *, activation=None) -> Tensor The `qlinear` operator. 2. ``qlinear(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, activation: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qlinear(input, weight, bias, activation, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qlinear` operator. ## `qlinear_` ```python qlinear_qlinear_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.QTensor ``` qlinear_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.QTensor qlinear_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor Overloaded function. 1. ``qlinear_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.QTensor`` qlinear_(input, weight, out, bias=None, *, activation=None) -> QTensor The `qlinear_` operator. 2. ``qlinear_(input: clika_runtime._core.QTensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor`` qlinear_(input, weight, out, bias=None, *, activation=None) -> Tensor The `qlinear_` operator. ## `qlinear_woq` ```python qlinear_woqqlinear_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor ``` qlinear_woq(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor qlinear_woq(input, weight, bias=None, *, activation=None, compute_mode='exact_fp') -> Tensor The `qlinear_woq` operator. ## `qlinear_woq_` ```python qlinear_woq_qlinear_woq_(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor ``` qlinear_woq_(input: clika_runtime._core.Tensor, weight: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor qlinear_woq_(input, weight, out, bias=None, *, activation=None, compute_mode='exact_fp') -> Tensor The `qlinear_woq_` operator. ## `qmatmul` ```python qmatmulqmatmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.Tensor ``` qmatmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.Tensor qmatmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, activation: object, transpose_a: bool, transpose_b: bool, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qmatmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.Tensor`` qmatmul(input, other, bias=None, *, activation=None, transpose_a=False, transpose_b=False) -> Tensor The `qmatmul` operator. 2. ``qmatmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None, activation: object, transpose_a: bool, transpose_b: bool, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qmatmul(input, other, bias, activation, transpose_a, transpose_b, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qmatmul` operator. ## `qmatmul_` ```python qmatmul_qmatmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.QTensor ``` qmatmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.QTensor qmatmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.Tensor qmatmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.Tensor Overloaded function. 1. ``qmatmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.QTensor`` qmatmul_(input, other, out, bias=None, *, activation=None, transpose_a=False, transpose_b=False) -> QTensor The `qmatmul_` operator. 2. ``qmatmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.Tensor`` qmatmul_(input, other, out, bias=None, *, activation=None, transpose_a=False, transpose_b=False) -> Tensor The `qmatmul_` operator. 3. ``qmatmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False) -> clika_runtime._core.Tensor`` qmatmul_(input, other, out, out_scale, out_zero_point, out_quant_axis=-1, bias=None, *, activation=None, transpose_a=False, transpose_b=False) -> Tensor The `qmatmul_` operator. ## `qmatmul_woq` ```python qmatmul_woqqmatmul_woq(input: clika_runtime._core.Tensor, other: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor ``` qmatmul_woq(input: clika_runtime._core.Tensor, other: clika_runtime._core.QTensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor qmatmul_woq(input, other, bias=None, *, activation=None, transpose_a=False, transpose_b=False, compute_mode='exact_fp') -> Tensor The `qmatmul_woq` operator. ## `qmatmul_woq_` ```python qmatmul_woq_qmatmul_woq_(input: clika_runtime._core.Tensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor ``` qmatmul_woq_(input: clika_runtime._core.Tensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, bias: clika_runtime._core.Tensor | None = None, *, activation: object | None = None, transpose_a: bool = False, transpose_b: bool = False, compute_mode: object = 'exact_fp') -> clika_runtime._core.Tensor qmatmul_woq_(input, other, out, bias=None, *, activation=None, transpose_a=False, transpose_b=False, compute_mode='exact_fp') -> Tensor The `qmatmul_woq_` operator. ## `qmoe` ```python qmoeqmoe(input: clika_runtime._core.QTensor, router_logits: clika_runtime._core.Tensor, fc1_experts: clika_runtime._core.QTensor, fc2_experts: clika_runtime._core.QTensor, top_k: int, fc1_bias: clika_runtime._core.Tensor | None = None, fc2_bias: clika_runtime._core.Tensor | None = None, fc3_experts: clika_runtime._core.QTensor = QTensor(, []), fc3_bias: clika_runtime._core.Tensor | None = None, e_score_correction_bias: clika_runtime._core.Tensor | None = None, router_weights: clika_runtime._core.Tensor | None = None, routing_mode: object | None = None, renormalize: bool | None = None, n_group: int | None = None, topk_group: int | None = None, routed_scaling_factor: float | None = None, sparse_mixer_eps: float | None = None, apply_router_weight_on_input: bool | None = None, *, activation: object | None = None, swiglu_fusion: object | None = None, swiglu_alpha: float | None = None, swiglu_beta: float | None = None, swiglu_limit: float | None = None, gelu_mode: object | None = None, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` qmoe(input: clika_runtime._core.QTensor, router_logits: clika_runtime._core.Tensor, fc1_experts: clika_runtime._core.QTensor, fc2_experts: clika_runtime._core.QTensor, top_k: int, fc1_bias: clika_runtime._core.Tensor | None = None, fc2_bias: clika_runtime._core.Tensor | None = None, fc3_experts: clika_runtime._core.QTensor = QTensor(, []), fc3_bias: clika_runtime._core.Tensor | None = None, e_score_correction_bias: clika_runtime._core.Tensor | None = None, router_weights: clika_runtime._core.Tensor | None = None, routing_mode: object | None = None, renormalize: bool | None = None, n_group: int | None = None, topk_group: int | None = None, routed_scaling_factor: float | None = None, sparse_mixer_eps: float | None = None, apply_router_weight_on_input: bool | None = None, *, activation: object | None = None, swiglu_fusion: object | None = None, swiglu_alpha: float | None = None, swiglu_beta: float | None = None, swiglu_limit: float | None = None, gelu_mode: object | None = None, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor qmoe(input: clika_runtime._core.QTensor, router_logits: clika_runtime._core.Tensor, fc1_experts: clika_runtime._core.QTensor, fc2_experts: clika_runtime._core.QTensor, top_k: int, fc1_bias: clika_runtime._core.Tensor | None, fc2_bias: clika_runtime._core.Tensor | None, fc3_experts: clika_runtime._core.QTensor, fc3_bias: clika_runtime._core.Tensor | None, e_score_correction_bias: clika_runtime._core.Tensor | None, router_weights: clika_runtime._core.Tensor | None, routing_mode: object, renormalize: bool | None, n_group: int | None, topk_group: int | None, routed_scaling_factor: float | None, sparse_mixer_eps: float | None, apply_router_weight_on_input: bool | None, activation: object, swiglu_fusion: object, swiglu_alpha: float | None, swiglu_beta: float | None, swiglu_limit: float | None, gelu_mode: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.QTensor Overloaded function. 1. ``qmoe(input: clika_runtime._core.QTensor, router_logits: clika_runtime._core.Tensor, fc1_experts: clika_runtime._core.QTensor, fc2_experts: clika_runtime._core.QTensor, top_k: int, fc1_bias: clika_runtime._core.Tensor | None = None, fc2_bias: clika_runtime._core.Tensor | None = None, fc3_experts: clika_runtime._core.QTensor = QTensor(, []), fc3_bias: clika_runtime._core.Tensor | None = None, e_score_correction_bias: clika_runtime._core.Tensor | None = None, router_weights: clika_runtime._core.Tensor | None = None, routing_mode: object | None = None, renormalize: bool | None = None, n_group: int | None = None, topk_group: int | None = None, routed_scaling_factor: float | None = None, sparse_mixer_eps: float | None = None, apply_router_weight_on_input: bool | None = None, *, activation: object | None = None, swiglu_fusion: object | None = None, swiglu_alpha: float | None = None, swiglu_beta: float | None = None, swiglu_limit: float | None = None, gelu_mode: object | None = None, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor`` qmoe(input, router_logits, fc1_experts, fc2_experts, top_k, fc1_bias=None, fc2_bias=None, fc3_experts=None, fc3_bias=None, e_score_correction_bias=None, router_weights=None, routing_mode=None, renormalize=None, n_group=None, topk_group=None, routed_scaling_factor=None, sparse_mixer_eps=None, apply_router_weight_on_input=None, *, activation=None, swiglu_fusion=None, swiglu_alpha=None, swiglu_beta=None, swiglu_limit=None, gelu_mode=None, shared_output=None) -> Tensor The `qmoe` operator. 2. ``qmoe(input: clika_runtime._core.QTensor, router_logits: clika_runtime._core.Tensor, fc1_experts: clika_runtime._core.QTensor, fc2_experts: clika_runtime._core.QTensor, top_k: int, fc1_bias: clika_runtime._core.Tensor | None, fc2_bias: clika_runtime._core.Tensor | None, fc3_experts: clika_runtime._core.QTensor, fc3_bias: clika_runtime._core.Tensor | None, e_score_correction_bias: clika_runtime._core.Tensor | None, router_weights: clika_runtime._core.Tensor | None, routing_mode: object, renormalize: bool | None, n_group: int | None, topk_group: int | None, routed_scaling_factor: float | None, sparse_mixer_eps: float | None, apply_router_weight_on_input: bool | None, activation: object, swiglu_fusion: object, swiglu_alpha: float | None, swiglu_beta: float | None, swiglu_limit: float | None, gelu_mode: object, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.QTensor`` qmoe(input, router_logits, fc1_experts, fc2_experts, top_k, fc1_bias, fc2_bias, fc3_experts, fc3_bias, e_score_correction_bias, router_weights, routing_mode, renormalize, n_group, topk_group, routed_scaling_factor, sparse_mixer_eps, apply_router_weight_on_input, activation, swiglu_fusion, swiglu_alpha, swiglu_beta, swiglu_limit, gelu_mode, out_scale, out_zero_point, out_quant_axis=-1, *, out_dtype=Undefined, shared_output=None) -> QTensor The `qmoe` operator. ## `qmoe_woq` ```python qmoe_woqqmoe_woq(input: clika_runtime._core.Tensor, router_logits: clika_runtime._core.Tensor, fc1_experts: clika_runtime._core.QTensor, fc2_experts: clika_runtime._core.QTensor, top_k: int, fc1_bias: clika_runtime._core.Tensor | None = None, fc2_bias: clika_runtime._core.Tensor | None = None, fc3_experts: clika_runtime._core.QTensor = QTensor(, []), fc3_bias: clika_runtime._core.Tensor | None = None, e_score_correction_bias: clika_runtime._core.Tensor | None = None, router_weights: clika_runtime._core.Tensor | None = None, routing_mode: object | None = None, renormalize: bool | None = None, n_group: int | None = None, topk_group: int | None = None, routed_scaling_factor: float | None = None, sparse_mixer_eps: float | None = None, apply_router_weight_on_input: bool | None = None, *, activation: object | None = None, swiglu_fusion: object | None = None, swiglu_alpha: float | None = None, swiglu_beta: float | None = None, swiglu_limit: float | None = None, gelu_mode: object | None = None, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` qmoe_woq(input: clika_runtime._core.Tensor, router_logits: clika_runtime._core.Tensor, fc1_experts: clika_runtime._core.QTensor, fc2_experts: clika_runtime._core.QTensor, top_k: int, fc1_bias: clika_runtime._core.Tensor | None = None, fc2_bias: clika_runtime._core.Tensor | None = None, fc3_experts: clika_runtime._core.QTensor = QTensor(, []), fc3_bias: clika_runtime._core.Tensor | None = None, e_score_correction_bias: clika_runtime._core.Tensor | None = None, router_weights: clika_runtime._core.Tensor | None = None, routing_mode: object | None = None, renormalize: bool | None = None, n_group: int | None = None, topk_group: int | None = None, routed_scaling_factor: float | None = None, sparse_mixer_eps: float | None = None, apply_router_weight_on_input: bool | None = None, *, activation: object | None = None, swiglu_fusion: object | None = None, swiglu_alpha: float | None = None, swiglu_beta: float | None = None, swiglu_limit: float | None = None, gelu_mode: object | None = None, shared_output: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor qmoe_woq(input, router_logits, fc1_experts, fc2_experts, top_k, fc1_bias=None, fc2_bias=None, fc3_experts=None, fc3_bias=None, e_score_correction_bias=None, router_weights=None, routing_mode=None, renormalize=None, n_group=None, topk_group=None, routed_scaling_factor=None, sparse_mixer_eps=None, apply_router_weight_on_input=None, *, activation=None, swiglu_fusion=None, swiglu_alpha=None, swiglu_beta=None, swiglu_limit=None, gelu_mode=None, shared_output=None) -> Tensor The `qmoe_woq` operator. ## `qmul` ```python qmulqmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor Overloaded function. 1. ``qmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qmul(input, other, *, activation='identity') -> Tensor The `qmul` operator. 2. ``qmul(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor`` qmul(input, other, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined, activation='identity') -> QTensor The `qmul` operator. ## `qmul_` ```python qmul_qmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor ``` qmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor qmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor Overloaded function. 1. ``qmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor`` qmul_(input, other, out, *, activation='identity') -> QTensor The `qmul_` operator. 2. ``qmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qmul_(input, other, out, *, activation='identity') -> Tensor The `qmul_` operator. 3. ``qmul_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qmul_(input, other, out, out_scale, out_zero_point=None, out_quant_axis=-1, *, activation='identity') -> Tensor The `qmul_` operator. ## `qneg` ```python qnegqneg(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor ``` qneg(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qneg(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qneg(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor`` qneg(input) -> Tensor The `qneg` operator. 2. ``qneg(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qneg(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qneg` operator. ## `qneg_` ```python qneg_qneg_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor ``` qneg_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor qneg_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor qneg_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor Overloaded function. 1. ``qneg_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor`` qneg_(input, out) -> QTensor The `qneg_` operator. 2. ``qneg_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` qneg_(input, out) -> Tensor The `qneg_` operator. 3. ``qneg_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor`` qneg_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor The `qneg_` operator. ## `qquick_gelu` ```python qquick_geluqquick_gelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor ``` qquick_gelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qquick_gelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qquick_gelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor`` qquick_gelu(input) -> Tensor The `qquick_gelu` operator. 2. ``qquick_gelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qquick_gelu(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qquick_gelu` operator. ## `qquick_gelu_` ```python qquick_gelu_qquick_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor ``` qquick_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor qquick_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor qquick_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor Overloaded function. 1. ``qquick_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor`` qquick_gelu_(input, out) -> QTensor The `qquick_gelu_` operator. 2. ``qquick_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` qquick_gelu_(input, out) -> Tensor The `qquick_gelu_` operator. 3. ``qquick_gelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor`` qquick_gelu_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor The `qquick_gelu_` operator. ## `qrelu` ```python qreluqrelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor ``` qrelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qrelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qrelu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor`` qrelu(input) -> Tensor The `qrelu` operator. 2. ``qrelu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qrelu(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qrelu` operator. ## `qrelu_` ```python qrelu_qrelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor ``` qrelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor qrelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor qrelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor Overloaded function. 1. ``qrelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor`` qrelu_(input, out) -> QTensor The `qrelu_` operator. 2. ``qrelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` qrelu_(input, out) -> Tensor The `qrelu_` operator. 3. ``qrelu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor`` qrelu_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor The `qrelu_` operator. ## `qsigmoid` ```python qsigmoidqsigmoid(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor ``` qsigmoid(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qsigmoid(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qsigmoid(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor`` qsigmoid(input) -> Tensor The `qsigmoid` operator. 2. ``qsigmoid(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qsigmoid(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qsigmoid` operator. ## `qsigmoid_` ```python qsigmoid_qsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor ``` qsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor qsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor qsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor Overloaded function. 1. ``qsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor`` qsigmoid_(input, out) -> QTensor The `qsigmoid_` operator. 2. ``qsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` qsigmoid_(input, out) -> Tensor The `qsigmoid_` operator. 3. ``qsigmoid_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor`` qsigmoid_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor The `qsigmoid_` operator. ## `qsilu` ```python qsiluqsilu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor ``` qsilu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qsilu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qsilu(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor`` qsilu(input) -> Tensor The `qsilu` operator. 2. ``qsilu(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qsilu(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qsilu` operator. ## `qsilu_` ```python qsilu_qsilu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor ``` qsilu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor qsilu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor qsilu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor Overloaded function. 1. ``qsilu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor`` qsilu_(input, out) -> QTensor The `qsilu_` operator. 2. ``qsilu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` qsilu_(input, out) -> Tensor The `qsilu_` operator. 3. ``qsilu_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor`` qsilu_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor The `qsilu_` operator. ## `qsub` ```python qsubqsub(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` qsub(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qsub(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor Overloaded function. 1. ``qsub(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qsub(input, other, *, activation='identity') -> Tensor The `qsub` operator. 2. ``qsub(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, activation: object | None = 'identity') -> clika_runtime._core.QTensor`` qsub(input, other, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined, activation='identity') -> QTensor The `qsub` operator. ## `qsub_` ```python qsub_qsub_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor ``` qsub_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor qsub_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor qsub_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor Overloaded function. 1. ``qsub_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor, *, activation: object | None = 'identity') -> clika_runtime._core.QTensor`` qsub_(input, other, out, *, activation='identity') -> QTensor The `qsub_` operator. 2. ``qsub_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qsub_(input, other, out, *, activation='identity') -> Tensor The `qsub_` operator. 3. ``qsub_(input: clika_runtime._core.QTensor, other: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor`` qsub_(input, other, out, out_scale, out_zero_point=None, out_quant_axis=-1, *, activation='identity') -> Tensor The `qsub_` operator. ## `qtanh` ```python qtanhqtanh(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor ``` qtanh(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor qtanh(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor Overloaded function. 1. ``qtanh(input: clika_runtime._core.QTensor) -> clika_runtime._core.Tensor`` qtanh(input) -> Tensor The `qtanh` operator. 2. ``qtanh(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor`` qtanh(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `qtanh` operator. ## `qtanh_` ```python qtanh_qtanh_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor ``` qtanh_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor qtanh_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor qtanh_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor Overloaded function. 1. ``qtanh_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor`` qtanh_(input, out) -> QTensor The `qtanh_` operator. 2. ``qtanh_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` qtanh_(input, out) -> Tensor The `qtanh_` operator. 3. ``qtanh_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor`` qtanh_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor The `qtanh_` operator. ## `quantile` ```python quantilequantile(input: clika_runtime._core.Tensor, q: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, keepdim: bool = False, interpolation: object = 'linear') -> clika_runtime._core.Tensor ``` quantile(input: clika_runtime._core.Tensor, q: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, keepdim: bool = False, interpolation: object = 'linear') -> clika_runtime._core.Tensor quantile(input, q, dim=None, keepdim=False, interpolation='linear') -> Tensor The `quantile` operator. ## `quantize` ```python quantizequantize(input: clika_runtime._core.Tensor, scale: clika_runtime._core.Tensor, zero_point: clika_runtime._core.Tensor | None = None, quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, block_size: int = 0) -> clika_runtime._core.QTensor ``` quantize(input: clika_runtime._core.Tensor, scale: clika_runtime._core.Tensor, zero_point: clika_runtime._core.Tensor | None = None, quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined, block_size: int = 0) -> clika_runtime._core.QTensor quantize(input, scale, zero_point=None, quant_axis=-1, *, out_dtype=Undefined, block_size=0) -> QTensor The `quantize` operator. ## `quantize_` ```python quantize_quantize_(input: clika_runtime._core.Tensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor ``` quantize_(input: clika_runtime._core.Tensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor quantize_(input: clika_runtime._core.Tensor, out: clika_runtime._core.Tensor, scale: clika_runtime._core.Tensor, zero_point: clika_runtime._core.Tensor | None = None, quant_axis: int = -1, block_size: int = 0) -> clika_runtime._core.Tensor Overloaded function. 1. ``quantize_(input: clika_runtime._core.Tensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor`` quantize_(input, out) -> QTensor The `quantize_` operator. 2. ``quantize_(input: clika_runtime._core.Tensor, out: clika_runtime._core.Tensor, scale: clika_runtime._core.Tensor, zero_point: clika_runtime._core.Tensor | None = None, quant_axis: int = -1, block_size: int = 0) -> clika_runtime._core.Tensor`` quantize_(input, out, scale, zero_point=None, quant_axis=-1, block_size=0) -> Tensor The `quantize_` operator. ## `quantize_dequantize` ```python quantize_dequantizequantize_dequantize(input: clika_runtime._core.Tensor, scale: clika_runtime._core.Tensor, zero_point: clika_runtime._core.Tensor | None = None, quant_axis: int = -1, *, code_dtype: clika_runtime._core.DataType = DataType.Undefined, out_dtype: clika_runtime._core.DataType = DataType.Undefined, block_size: int = 0) -> clika_runtime._core.Tensor ``` quantize_dequantize(input: clika_runtime._core.Tensor, scale: clika_runtime._core.Tensor, zero_point: clika_runtime._core.Tensor | None = None, quant_axis: int = -1, *, code_dtype: clika_runtime._core.DataType = DataType.Undefined, out_dtype: clika_runtime._core.DataType = DataType.Undefined, block_size: int = 0) -> clika_runtime._core.Tensor quantize_dequantize(input, scale, zero_point=None, quant_axis=-1, *, code_dtype=Undefined, out_dtype=Undefined, block_size=0) -> Tensor The `quantize_dequantize` operator. ## `quantize_to_scheme` ```python quantize_to_schemequantize_to_scheme(input: clika_runtime._core.Tensor, scheme: str, scale: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.QTensor ``` quantize_to_scheme(input: clika_runtime._core.Tensor, scheme: str, scale: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.QTensor quantize_to_scheme(input, scheme, scale=None) -> QTensor The `quantize_to_scheme` operator. ## `quick_gelu` ```python quick_geluquick_gelu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` quick_gelu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor quick_gelu(input) -> Tensor The `quick_gelu` operator. ## `rad2deg` ```python rad2degrad2deg(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` rad2deg(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor rad2deg(input) -> Tensor The `rad2deg` operator. ## `rad2deg_` ```python rad2deg_rad2deg_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` rad2deg_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor rad2deg_(self) -> Tensor The `rad2deg_` operator. ## `rand` ```python randrand(shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], *, dtype: clika_runtime._core.DataType = DataType.Float32, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` rand(shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], *, dtype: clika_runtime._core.DataType = DataType.Float32, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor rand(shape, *, dtype=Float32, device=None) -> Tensor The `rand` operator. ## `rand_like` ```python rand_likerand_like(reference: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` rand_like(reference: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor rand_like(reference, *, device=None) -> Tensor The `rand_like` operator. ## `randint` ```python randintrandint(low: int, high: int, shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], *, dtype: clika_runtime._core.DataType = DataType.Int64, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` randint(low: int, high: int, shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], *, dtype: clika_runtime._core.DataType = DataType.Int64, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor randint(low, high, shape, *, dtype=Int64, device=None) -> Tensor The `randint` operator. ## `randint_like` ```python randint_likerandint_like(reference: clika_runtime._core.Tensor, low: int, high: int, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` randint_like(reference: clika_runtime._core.Tensor, low: int, high: int, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor randint_like(reference, low, high, *, device=None) -> Tensor The `randint_like` operator. ## `randn` ```python randnrandn(shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], *, dtype: clika_runtime._core.DataType = DataType.Float32, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` randn(shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], *, dtype: clika_runtime._core.DataType = DataType.Float32, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor randn(shape, *, dtype=Float32, device=None) -> Tensor The `randn` operator. ## `randn_like` ```python randn_likerandn_like(reference: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` randn_like(reference: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor randn_like(reference, *, device=None) -> Tensor The `randn_like` operator. ## `random_` ```python random_random_(self: clika_runtime._core.Tensor, low: int | None = None, high: int | None = None, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` random_(self: clika_runtime._core.Tensor, low: int | None = None, high: int | None = None, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor random_(self, low=None, high=None, *, device=None) -> Tensor The `random_` operator. ## `randperm` ```python randpermrandperm(n: clika_runtime._core.ops.ScalarOrTensor, *, dtype: clika_runtime._core.DataType = DataType.Int64, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` randperm(n: clika_runtime._core.ops.ScalarOrTensor, *, dtype: clika_runtime._core.DataType = DataType.Int64, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor randperm(n, *, dtype=Int64, device=None) -> Tensor The `randperm` operator. ## `reciprocal` ```python reciprocalreciprocal(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` reciprocal(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor reciprocal(input) -> Tensor The `reciprocal` operator. ## `reciprocal_` ```python reciprocal_reciprocal_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` reciprocal_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor reciprocal_(self) -> Tensor The `reciprocal_` operator. ## `reflect_pad` ```python reflect_padreflect_pad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor ``` reflect_pad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor reflect_pad(input, pad) -> Tensor The `reflect_pad` operator. ## `reglu` ```python reglureglu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` reglu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor reglu(input) -> Tensor The `reglu` operator. ## `relu` ```python relurelu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` relu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor relu(input) -> Tensor The `relu` operator. ## `relu6` ```python relu6relu6(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` relu6(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor relu6(input) -> Tensor The `relu6` operator. ## `relu6_` ```python relu6_relu6_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` relu6_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor relu6_(self) -> Tensor The `relu6_` operator. ## `relu_` ```python relu_relu_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` relu_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor relu_(self) -> Tensor The `relu_` operator. ## `remainder` ```python remainderremainder(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` remainder(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor remainder(input, other) -> Tensor The `remainder` operator. ## `remainder_` ```python remainder_remainder_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` remainder_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor remainder_(self, other) -> Tensor The `remainder_` operator. ## `renorm` ```python renormrenorm(input: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar, dim: int, maxnorm: clika_runtime._core.ops.Scalar, eps: float | None = None) -> clika_runtime._core.Tensor ``` renorm(input: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar, dim: int, maxnorm: clika_runtime._core.ops.Scalar, eps: float | None = None) -> clika_runtime._core.Tensor renorm(input, p, dim, maxnorm, eps=None) -> Tensor The `renorm` operator. ## `renorm_` ```python renorm_renorm_(self: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar, dim: int, maxnorm: clika_runtime._core.ops.Scalar, eps: float | None = None) -> clika_runtime._core.Tensor ``` renorm_(self: clika_runtime._core.Tensor, p: clika_runtime._core.ops.Scalar, dim: int, maxnorm: clika_runtime._core.ops.Scalar, eps: float | None = None) -> clika_runtime._core.Tensor renorm_(self, p, dim, maxnorm, eps=None) -> Tensor The `renorm_` operator. ## `repeat` ```python repeatrepeat(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor ``` repeat(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor repeat(input, sizes) -> Tensor The `repeat` operator. ## `repeat_interleave` ```python repeat_interleaverepeat_interleave(input: clika_runtime._core.Tensor, repeats: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, output_size: int | None = None) -> clika_runtime._core.Tensor ``` repeat_interleave(input: clika_runtime._core.Tensor, repeats: clika_runtime._core.ops.ScalarOrTensor, dim: int | None = None, output_size: int | None = None) -> clika_runtime._core.Tensor repeat_interleave(input, repeats, dim=None, output_size=None) -> Tensor The `repeat_interleave` operator. ## `replicate_pad` ```python replicate_padreplicate_pad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor ``` replicate_pad(input: clika_runtime._core.Tensor, pad: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor replicate_pad(input, pad) -> Tensor The `replicate_pad` operator. ## `requantize` ```python requantizerequantize(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor ``` requantize(input: clika_runtime._core.QTensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1, *, out_dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.QTensor requantize(input, out_scale, out_zero_point=None, out_quant_axis=-1, *, out_dtype=Undefined) -> QTensor The `requantize` operator. ## `requantize_` ```python requantize_requantize_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor ``` requantize_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor requantize_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor Overloaded function. 1. ``requantize_(input: clika_runtime._core.QTensor, out: clika_runtime._core.QTensor) -> clika_runtime._core.QTensor`` requantize_(input, out) -> QTensor The `requantize_` operator. 2. ``requantize_(input: clika_runtime._core.QTensor, out: clika_runtime._core.Tensor, out_scale: clika_runtime._core.Tensor, out_zero_point: clika_runtime._core.Tensor | None = None, out_quant_axis: int = -1) -> clika_runtime._core.Tensor`` requantize_(input, out, out_scale, out_zero_point=None, out_quant_axis=-1) -> Tensor The `requantize_` operator. ## `resample` ```python resampleresample(input: clika_runtime._core.Tensor, orig_freq: int, new_freq: int, lowpass_filter_width: int = 16, rolloff: float = 0.945, beta: float | None = None) -> clika_runtime._core.Tensor ``` resample(input: clika_runtime._core.Tensor, orig_freq: int, new_freq: int, lowpass_filter_width: int = 16, rolloff: float = 0.945, beta: float | None = None) -> clika_runtime._core.Tensor resample(input, orig_freq, new_freq, lowpass_filter_width=16, rolloff=0.945, beta=None) -> Tensor The `resample` operator. ## `reshape` ```python reshapereshape(input: clika_runtime._core.Tensor, shape: collections.abc.Sequence[clika_runtime._core.ops.IndexBound]) -> clika_runtime._core.Tensor ``` reshape(input: clika_runtime._core.Tensor, shape: collections.abc.Sequence[clika_runtime._core.ops.IndexBound]) -> clika_runtime._core.Tensor reshape(input, shape) -> Tensor The `reshape` operator. ## `reshape_as` ```python reshape_asreshape_as(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` reshape_as(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor reshape_as(input, other) -> Tensor The `reshape_as` operator. ## `rfft` ```python rfftrfft(input: clika_runtime._core.Tensor, n_fft: int | None = None, normalized: bool = False) -> clika_runtime._core.Tensor ``` rfft(input: clika_runtime._core.Tensor, n_fft: int | None = None, normalized: bool = False) -> clika_runtime._core.Tensor rfft(input, n_fft=None, normalized=False) -> Tensor The `rfft` operator. ## `rms_norm` ```python rms_normrms_norm(input: clika_runtime._core.Tensor, normalized_shape: collections.abc.Sequence[int], weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor ``` rms_norm(input: clika_runtime._core.Tensor, normalized_shape: collections.abc.Sequence[int], weight: clika_runtime._core.Tensor | None = None, bias: clika_runtime._core.Tensor | None = None, eps: float | None = None, *, activation: object | None = None) -> clika_runtime._core.Tensor rms_norm(input, normalized_shape, weight=None, bias=None, eps=None, *, activation=None) -> Tensor The `rms_norm` operator. ## `roll` ```python rollroll(input: clika_runtime._core.Tensor, shifts: collections.abc.Sequence[int], dims: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor ``` roll(input: clika_runtime._core.Tensor, shifts: collections.abc.Sequence[int], dims: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor roll(input, shifts, dims=[]) -> Tensor The `roll` operator. ## `rot90` ```python rot90rot90(input: clika_runtime._core.Tensor, k: int = 1, dims: collections.abc.Sequence[int] = [0, 1]) -> clika_runtime._core.Tensor ``` rot90(input: clika_runtime._core.Tensor, k: int = 1, dims: collections.abc.Sequence[int] = [0, 1]) -> clika_runtime._core.Tensor rot90(input, k=1, dims=[0, 1]) -> Tensor The `rot90` operator. ## `rotary_embedding` ```python rotary_embeddingrotary_embedding(input: clika_runtime._core.Tensor, position_ids: clika_runtime._core.Tensor | None = None, cos: clika_runtime._core.Tensor | None = None, sin: clika_runtime._core.Tensor | None = None, mode: object | None = None, rotary_dim: int | None = None, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` rotary_embedding(input: clika_runtime._core.Tensor, position_ids: clika_runtime._core.Tensor | None = None, cos: clika_runtime._core.Tensor | None = None, sin: clika_runtime._core.Tensor | None = None, mode: object | None = None, rotary_dim: int | None = None, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor rotary_embedding(input, position_ids=None, cos=None, sin=None, mode=None, rotary_dim=None, theta=None, scaling=None, scale=None, low_freq_factor=None, high_freq_factor=None, original_max_pos=None, beta_fast=None, beta_slow=None, freq_factors=None) -> Tensor The `rotary_embedding` operator. ## `rotary_embedding_qk` ```python rotary_embedding_qkrotary_embedding_qk(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, position_ids: clika_runtime._core.Tensor | None = None, cos: clika_runtime._core.Tensor | None = None, sin: clika_runtime._core.Tensor | None = None, mode: object | None = None, rotary_dim: int | None = None, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` rotary_embedding_qk(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, position_ids: clika_runtime._core.Tensor | None = None, cos: clika_runtime._core.Tensor | None = None, sin: clika_runtime._core.Tensor | None = None, mode: object | None = None, rotary_dim: int | None = None, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] rotary_embedding_qk(query, key, position_ids=None, cos=None, sin=None, mode=None, rotary_dim=None, theta=None, scaling=None, scale=None, low_freq_factor=None, high_freq_factor=None, original_max_pos=None, beta_fast=None, beta_slow=None, freq_factors=None) -> tuple[Tensor, Tensor] The `rotary_embedding_qk` operator. ## `rotary_embedding_qk_varlen` ```python rotary_embedding_qk_varlenrotary_embedding_qk_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, cu_seqlens: clika_runtime._core.Tensor, seqlens: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, cos: clika_runtime._core.Tensor | None = None, sin: clika_runtime._core.Tensor | None = None, mode: object | None = None, rotary_dim: int | None = None, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` rotary_embedding_qk_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, cu_seqlens: clika_runtime._core.Tensor, seqlens: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, cos: clika_runtime._core.Tensor | None = None, sin: clika_runtime._core.Tensor | None = None, mode: object | None = None, rotary_dim: int | None = None, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] rotary_embedding_qk_varlen(query, key, cu_seqlens, seqlens=None, position_ids=None, cos=None, sin=None, mode=None, rotary_dim=None, theta=None, scaling=None, scale=None, low_freq_factor=None, high_freq_factor=None, original_max_pos=None, beta_fast=None, beta_slow=None, freq_factors=None) -> tuple[Tensor, Tensor] The `rotary_embedding_qk_varlen` operator. ## `rotary_embedding_varlen` ```python rotary_embedding_varlenrotary_embedding_varlen(input: clika_runtime._core.Tensor, cu_seqlens: clika_runtime._core.Tensor, seqlens: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, cos: clika_runtime._core.Tensor | None = None, sin: clika_runtime._core.Tensor | None = None, mode: object | None = None, rotary_dim: int | None = None, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` rotary_embedding_varlen(input: clika_runtime._core.Tensor, cu_seqlens: clika_runtime._core.Tensor, seqlens: clika_runtime._core.Tensor | None = None, position_ids: clika_runtime._core.Tensor | None = None, cos: clika_runtime._core.Tensor | None = None, sin: clika_runtime._core.Tensor | None = None, mode: object | None = None, rotary_dim: int | None = None, theta: float | None = None, scaling: object | None = None, scale: float | None = None, low_freq_factor: float | None = None, high_freq_factor: float | None = None, original_max_pos: int | None = None, beta_fast: float | None = None, beta_slow: float | None = None, freq_factors: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor rotary_embedding_varlen(input, cu_seqlens, seqlens=None, position_ids=None, cos=None, sin=None, mode=None, rotary_dim=None, theta=None, scaling=None, scale=None, low_freq_factor=None, high_freq_factor=None, original_max_pos=None, beta_fast=None, beta_slow=None, freq_factors=None) -> Tensor The `rotary_embedding_varlen` operator. ## `round` ```python roundround(input: clika_runtime._core.Tensor, decimals: int = 0) -> clika_runtime._core.Tensor ``` round(input: clika_runtime._core.Tensor, decimals: int = 0) -> clika_runtime._core.Tensor round(input, decimals=0) -> Tensor The `round` operator. ## `round_` ```python round_round_(self: clika_runtime._core.Tensor, decimals: int = 0) -> clika_runtime._core.Tensor ``` round_(self: clika_runtime._core.Tensor, decimals: int = 0) -> clika_runtime._core.Tensor round_(self, decimals=0) -> Tensor The `round_` operator. ## `rsqrt` ```python rsqrtrsqrt(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` rsqrt(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor rsqrt(input) -> Tensor The `rsqrt` operator. ## `rsqrt_` ```python rsqrt_rsqrt_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` rsqrt_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor rsqrt_(self) -> Tensor The `rsqrt_` operator. ## `scaled_dot_product_attention` ```python scaled_dot_product_attentionscaled_dot_product_attention(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, attn_mask: clika_runtime._core.Tensor | None = None, is_causal: bool = False, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...)) -> clika_runtime._core.Tensor ``` scaled_dot_product_attention(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, attn_mask: clika_runtime._core.Tensor | None = None, is_causal: bool = False, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...)) -> clika_runtime._core.Tensor scaled_dot_product_attention(query, key, value, attn_mask=None, is_causal=False, q_scale=None, k_scale=None, v_scale=None) -> Tensor The `scaled_dot_product_attention` operator. ## `scaled_dot_product_attention_varlen` ```python scaled_dot_product_attention_varlenscaled_dot_product_attention_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, cu_seqlens_k: clika_runtime._core.Tensor, max_seqlen_q: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), max_seqlen_k: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), attn_mask: clika_runtime._core.Tensor | None = None, is_causal: bool = False, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...)) -> clika_runtime._core.Tensor ``` scaled_dot_product_attention_varlen(query: clika_runtime._core.Tensor, key: clika_runtime._core.Tensor, value: clika_runtime._core.Tensor, cu_seqlens_q: clika_runtime._core.Tensor, cu_seqlens_k: clika_runtime._core.Tensor, max_seqlen_q: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), max_seqlen_k: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), attn_mask: clika_runtime._core.Tensor | None = None, is_causal: bool = False, q_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), k_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...), v_scale: clika_runtime._core.ops.ScalarOrTensor = ScalarOrTensor(...)) -> clika_runtime._core.Tensor scaled_dot_product_attention_varlen(query, key, value, cu_seqlens_q, cu_seqlens_k, max_seqlen_q=None, max_seqlen_k=None, attn_mask=None, is_causal=False, q_scale=None, k_scale=None, v_scale=None) -> Tensor The `scaled_dot_product_attention_varlen` operator. ## `scatter` ```python scatterscatter(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` scatter(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor scatter(input, dim, index, src) -> Tensor The `scatter` operator. ## `scatter_` ```python scatter_scatter_(self: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` scatter_(self: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor scatter_(self, dim, index, src) -> Tensor The `scatter_` operator. ## `scatter_add` ```python scatter_addscatter_add(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, deterministic: bool = False) -> clika_runtime._core.Tensor ``` scatter_add(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, deterministic: bool = False) -> clika_runtime._core.Tensor scatter_add(input, dim, index, src, deterministic=False) -> Tensor The `scatter_add` operator. ## `scatter_add_` ```python scatter_add_scatter_add_(self: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, deterministic: bool = False) -> clika_runtime._core.Tensor ``` scatter_add_(self: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, deterministic: bool = False) -> clika_runtime._core.Tensor scatter_add_(self, dim, index, src, deterministic=False) -> Tensor The `scatter_add_` operator. ## `scatter_reduce` ```python scatter_reducescatter_reduce(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, reduce: object, include_self: bool = True, deterministic: bool = False) -> clika_runtime._core.Tensor ``` scatter_reduce(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, reduce: object, include_self: bool = True, deterministic: bool = False) -> clika_runtime._core.Tensor scatter_reduce(input, dim, index, src, reduce, include_self=True, deterministic=False) -> Tensor The `scatter_reduce` operator. ## `scatter_reduce_` ```python scatter_reduce_scatter_reduce_(self: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, reduce: object, include_self: bool = True, deterministic: bool = False) -> clika_runtime._core.Tensor ``` scatter_reduce_(self: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.Tensor, src: clika_runtime._core.Tensor, reduce: object, include_self: bool = True, deterministic: bool = False) -> clika_runtime._core.Tensor scatter_reduce_(self, dim, index, src, reduce, include_self=True, deterministic=False) -> Tensor The `scatter_reduce_` operator. ## `searchsorted` ```python searchsortedsearchsorted(sorted_sequence: clika_runtime._core.Tensor, values: clika_runtime._core.Tensor, out_int32: bool = False, right: bool = False, side: bool | None = None, sorter: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor ``` searchsorted(sorted_sequence: clika_runtime._core.Tensor, values: clika_runtime._core.Tensor, out_int32: bool = False, right: bool = False, side: bool | None = None, sorter: clika_runtime._core.Tensor | None = None) -> clika_runtime._core.Tensor searchsorted(sorted_sequence, values, out_int32=False, right=False, side=None, sorter=None) -> Tensor The `searchsorted` operator. ## `select` ```python selectselect(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.ops.IndexBound) -> clika_runtime._core.Tensor ``` select(input: clika_runtime._core.Tensor, dim: int, index: clika_runtime._core.ops.IndexBound) -> clika_runtime._core.Tensor select(input, dim, index) -> Tensor The `select` operator. ## `selu` ```python seluselu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` selu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor selu(input) -> Tensor The `selu` operator. ## `selu_` ```python selu_selu_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` selu_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor selu_(self) -> Tensor The `selu_` operator. ## `sgn` ```python sgnsgn(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sgn(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sgn(input) -> Tensor The `sgn` operator. ## `sgn_` ```python sgn_sgn_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sgn_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sgn_(self) -> Tensor The `sgn_` operator. ## `shape` ```python shapeshape(input: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor ``` shape(input: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor shape(input: clika_runtime._core.Tensor, start: int | None, end: int | None) -> clika_runtime._core.Tensor Overloaded function. 1. ``shape(input: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor`` shape(input, dim=None) -> Tensor The `shape` operator. 2. ``shape(input: clika_runtime._core.Tensor, start: int | None, end: int | None) -> clika_runtime._core.Tensor`` shape(input, start, end) -> Tensor The `shape` operator. ## `shape_host` ```python shape_hostshape_host(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` shape_host(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor shape_host(input: clika_runtime._core.Tensor, dim: int) -> clika_runtime._core.Tensor shape_host(input: clika_runtime._core.Tensor, start: int | None, end: int | None) -> clika_runtime._core.Tensor Overloaded function. 1. ``shape_host(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` shape_host(input) -> Tensor The `shape_host` operator. 2. ``shape_host(input: clika_runtime._core.Tensor, dim: int) -> clika_runtime._core.Tensor`` shape_host(input, dim) -> Tensor The `shape_host` operator. 3. ``shape_host(input: clika_runtime._core.Tensor, start: int | None, end: int | None) -> clika_runtime._core.Tensor`` shape_host(input, start, end) -> Tensor The `shape_host` operator. ## `sigmoid` ```python sigmoidsigmoid(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sigmoid(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sigmoid(input) -> Tensor The `sigmoid` operator. ## `sigmoid_` ```python sigmoid_sigmoid_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sigmoid_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sigmoid_(self) -> Tensor The `sigmoid_` operator. ## `sign` ```python signsign(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sign(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sign(input) -> Tensor The `sign` operator. ## `sign_` ```python sign_sign_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sign_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sign_(self) -> Tensor The `sign_` operator. ## `silu` ```python silusilu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` silu(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor silu(input) -> Tensor The `silu` operator. ## `silu_` ```python silu_silu_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` silu_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor silu_(self) -> Tensor The `silu_` operator. ## `sin` ```python sinsin(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sin(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sin(input) -> Tensor The `sin` operator. ## `sin_` ```python sin_sin_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sin_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sin_(self) -> Tensor The `sin_` operator. ## `sinc` ```python sincsinc(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sinc(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sinc(input) -> Tensor The `sinc` operator. ## `sinc_` ```python sinc_sinc_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sinc_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sinc_(self) -> Tensor The `sinc_` operator. ## `sinh` ```python sinhsinh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sinh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sinh(input) -> Tensor The `sinh` operator. ## `sinh_` ```python sinh_sinh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sinh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sinh_(self) -> Tensor The `sinh_` operator. ## `slice` ```python sliceslice(input: clika_runtime._core.Tensor, dim: int, start: clika_runtime._core.ops.IndexBound = IndexBound(...), end: clika_runtime._core.ops.IndexBound = IndexBound(...), step: clika_runtime._core.ops.IndexBound = IndexBound(...)) -> clika_runtime._core.Tensor ``` slice(input: clika_runtime._core.Tensor, dim: int, start: clika_runtime._core.ops.IndexBound = IndexBound(...), end: clika_runtime._core.ops.IndexBound = IndexBound(...), step: clika_runtime._core.ops.IndexBound = IndexBound(...)) -> clika_runtime._core.Tensor slice(input: clika_runtime._core.Tensor, dim: collections.abc.Sequence[int], start: collections.abc.Sequence[clika_runtime._core.ops.IndexBound] = [], end: collections.abc.Sequence[clika_runtime._core.ops.IndexBound] = [], step: collections.abc.Sequence[clika_runtime._core.ops.IndexBound] = []) -> clika_runtime._core.Tensor Overloaded function. 1. ``slice(input: clika_runtime._core.Tensor, dim: int, start: clika_runtime._core.ops.IndexBound = IndexBound(...), end: clika_runtime._core.ops.IndexBound = IndexBound(...), step: clika_runtime._core.ops.IndexBound = IndexBound(...)) -> clika_runtime._core.Tensor`` slice(input, dim, start=None, end=None, step=1) -> Tensor The `slice` operator. 2. ``slice(input: clika_runtime._core.Tensor, dim: collections.abc.Sequence[int], start: collections.abc.Sequence[clika_runtime._core.ops.IndexBound] = [], end: collections.abc.Sequence[clika_runtime._core.ops.IndexBound] = [], step: collections.abc.Sequence[clika_runtime._core.ops.IndexBound] = []) -> clika_runtime._core.Tensor`` slice(input, dim, start=[], end=[], step=[]) -> Tensor The `slice` operator. ## `smooth_l1_loss` ```python smooth_l1_losssmooth_l1_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean', beta: float = 1.0) -> clika_runtime._core.Tensor ``` smooth_l1_loss(input: clika_runtime._core.Tensor, target: clika_runtime._core.Tensor, reduction: object = 'mean', beta: float = 1.0) -> clika_runtime._core.Tensor smooth_l1_loss(input, target, reduction='mean', beta=1.0) -> Tensor The `smooth_l1_loss` operator. ## `snake` ```python snakesnake(input: clika_runtime._core.Tensor, alpha: clika_runtime._core.Tensor | None = None, beta: clika_runtime._core.Tensor | None = None, eps: float = 1e-09) -> clika_runtime._core.Tensor ``` snake(input: clika_runtime._core.Tensor, alpha: clika_runtime._core.Tensor | None = None, beta: clika_runtime._core.Tensor | None = None, eps: float = 1e-09) -> clika_runtime._core.Tensor snake(input, alpha=None, beta=None, eps=1e-9) -> Tensor The `snake` operator. ## `snake_` ```python snake_snake_(self: clika_runtime._core.Tensor, alpha: clika_runtime._core.Tensor | None = None, beta: clika_runtime._core.Tensor | None = None, eps: float = 1e-09) -> clika_runtime._core.Tensor ``` snake_(self: clika_runtime._core.Tensor, alpha: clika_runtime._core.Tensor | None = None, beta: clika_runtime._core.Tensor | None = None, eps: float = 1e-09) -> clika_runtime._core.Tensor snake_(self, alpha=None, beta=None, eps=1e-9) -> Tensor The `snake_` operator. ## `softcap_logits` ```python softcap_logitssoftcap_logits(input: clika_runtime._core.Tensor, cap: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` softcap_logits(input: clika_runtime._core.Tensor, cap: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor softcap_logits(input, cap) -> Tensor The `softcap_logits` operator. ## `softcap_logits_` ```python softcap_logits_softcap_logits_(self: clika_runtime._core.Tensor, cap: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` softcap_logits_(self: clika_runtime._core.Tensor, cap: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor softcap_logits_(self, cap) -> Tensor The `softcap_logits_` operator. ## `softmax` ```python softmaxsoftmax(input: clika_runtime._core.Tensor, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor ``` softmax(input: clika_runtime._core.Tensor, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor softmax(input, dim=-1, *, dtype=Undefined) -> Tensor The `softmax` operator. ## `softmax_` ```python softmax_softmax_(self: clika_runtime._core.Tensor, dim: int = -1) -> clika_runtime._core.Tensor ``` softmax_(self: clika_runtime._core.Tensor, dim: int = -1) -> clika_runtime._core.Tensor softmax_(self, dim=-1) -> Tensor The `softmax_` operator. ## `softmin` ```python softminsoftmin(input: clika_runtime._core.Tensor, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor ``` softmin(input: clika_runtime._core.Tensor, dim: int = -1, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor softmin(input, dim=-1, *, dtype=Undefined) -> Tensor The `softmin` operator. ## `softmin_` ```python softmin_softmin_(self: clika_runtime._core.Tensor, dim: int = -1) -> clika_runtime._core.Tensor ``` softmin_(self: clika_runtime._core.Tensor, dim: int = -1) -> clika_runtime._core.Tensor softmin_(self, dim=-1) -> Tensor The `softmin_` operator. ## `softplus` ```python softplussoftplus(input: clika_runtime._core.Tensor, beta: float = 1.0, threshold: float = 20.0) -> clika_runtime._core.Tensor ``` softplus(input: clika_runtime._core.Tensor, beta: float = 1.0, threshold: float = 20.0) -> clika_runtime._core.Tensor softplus(input, beta=1.0, threshold=20.0) -> Tensor The `softplus` operator. ## `softplus_` ```python softplus_softplus_(self: clika_runtime._core.Tensor, beta: float = 1.0, threshold: float = 20.0) -> clika_runtime._core.Tensor ``` softplus_(self: clika_runtime._core.Tensor, beta: float = 1.0, threshold: float = 20.0) -> clika_runtime._core.Tensor softplus_(self, beta=1.0, threshold=20.0) -> Tensor The `softplus_` operator. ## `softshrink` ```python softshrinksoftshrink(input: clika_runtime._core.Tensor, lambd: float = 0.5) -> clika_runtime._core.Tensor ``` softshrink(input: clika_runtime._core.Tensor, lambd: float = 0.5) -> clika_runtime._core.Tensor softshrink(input, lambd=0.5) -> Tensor The `softshrink` operator. ## `softshrink_` ```python softshrink_softshrink_(self: clika_runtime._core.Tensor, lambd: float = 0.5) -> clika_runtime._core.Tensor ``` softshrink_(self: clika_runtime._core.Tensor, lambd: float = 0.5) -> clika_runtime._core.Tensor softshrink_(self, lambd=0.5) -> Tensor The `softshrink_` operator. ## `softsign` ```python softsignsoftsign(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` softsign(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor softsign(input) -> Tensor The `softsign` operator. ## `softsign_` ```python softsign_softsign_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` softsign_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor softsign_(self) -> Tensor The `softsign_` operator. ## `sort` ```python sortsort(input: clika_runtime._core.Tensor, dim: int = -1, descending: bool = False, stable: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` sort(input: clika_runtime._core.Tensor, dim: int = -1, descending: bool = False, stable: bool = False) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] sort(input, dim=-1, descending=False, stable=False) -> tuple[Tensor, Tensor] The `sort` operator. ## `split_by_size` ```python split_by_sizesplit_by_size(input: clika_runtime._core.Tensor, chunk_size: int, dim: int = 0) -> list[clika_runtime._core.Tensor] ``` split_by_size(input: clika_runtime._core.Tensor, chunk_size: int, dim: int = 0) -> list[clika_runtime._core.Tensor] split_by_size(input, chunk_size, dim=0) -> list[Tensor] The `split_by_size` operator. ## `split_with_sizes` ```python split_with_sizessplit_with_sizes(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], dim: int = 0) -> list[clika_runtime._core.Tensor] ``` split_with_sizes(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], dim: int = 0) -> list[clika_runtime._core.Tensor] split_with_sizes(input, sizes, dim=0) -> list[Tensor] The `split_with_sizes` operator. ## `sqrt` ```python sqrtsqrt(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sqrt(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sqrt(input) -> Tensor The `sqrt` operator. ## `sqrt_` ```python sqrt_sqrt_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` sqrt_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor sqrt_(self) -> Tensor The `sqrt_` operator. ## `square` ```python squaresquare(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` square(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor square(input) -> Tensor The `square` operator. ## `square_` ```python square_square_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` square_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor square_(self) -> Tensor The `square_` operator. ## `squeeze` ```python squeezesqueeze(input: clika_runtime._core.Tensor, dim: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor ``` squeeze(input: clika_runtime._core.Tensor, dim: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor squeeze(input, dim=[]) -> Tensor The `squeeze` operator. ## `squeeze_` ```python squeeze_squeeze_(self: clika_runtime._core.Tensor, dim: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor ``` squeeze_(self: clika_runtime._core.Tensor, dim: collections.abc.Sequence[int] = []) -> clika_runtime._core.Tensor squeeze_(self, dim=[]) -> Tensor The `squeeze_` operator. ## `ssd_update` ```python ssd_updatessd_update(input: clika_runtime._core.Tensor, dt: clika_runtime._core.Tensor, a_rate: clika_runtime._core.Tensor, b_mat: clika_runtime._core.Tensor, c_mat: clika_runtime._core.Tensor, d_skip: clika_runtime._core.Tensor | None, dt_bias: clika_runtime._core.Tensor | None, gate: clika_runtime._core.Tensor | None, state: clika_runtime._core.Tensor, seq_lens: clika_runtime._core.Tensor | None = None, slot_ids: clika_runtime._core.Tensor | None = None, dt_softplus: bool = False) -> clika_runtime._core.Tensor ``` ssd_update(input: clika_runtime._core.Tensor, dt: clika_runtime._core.Tensor, a_rate: clika_runtime._core.Tensor, b_mat: clika_runtime._core.Tensor, c_mat: clika_runtime._core.Tensor, d_skip: clika_runtime._core.Tensor | None, dt_bias: clika_runtime._core.Tensor | None, gate: clika_runtime._core.Tensor | None, state: clika_runtime._core.Tensor, seq_lens: clika_runtime._core.Tensor | None = None, slot_ids: clika_runtime._core.Tensor | None = None, dt_softplus: bool = False) -> clika_runtime._core.Tensor ssd_update(input, dt, a_rate, b_mat, c_mat, d_skip, dt_bias, gate, state, seq_lens=None, slot_ids=None, dt_softplus=False) -> Tensor The `ssd_update` operator. ## `stack` ```python stackstack(tensors: collections.abc.Sequence[clika_runtime._core.Tensor], dim: int = 0) -> clika_runtime._core.Tensor ``` stack(tensors: collections.abc.Sequence[clika_runtime._core.Tensor], dim: int = 0) -> clika_runtime._core.Tensor stack(tensors, dim=0) -> Tensor The `stack` operator. ## `std` ```python stdstd(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], correction: int = 1, keepdim: bool = False) -> clika_runtime._core.Tensor ``` std(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], correction: int = 1, keepdim: bool = False) -> clika_runtime._core.Tensor std(input, dims=[], correction=1, keepdim=False) -> Tensor The `std` operator. ## `stft` ```python stftstft(input: clika_runtime._core.Tensor, n_fft: int, hop_length: int | None = None, win_length: int | None = None, window: clika_runtime._core.Tensor | None = None, center: bool = True, pad_mode: object = 'reflect', normalized: bool = False, onesided: bool = True) -> clika_runtime._core.Tensor ``` stft(input: clika_runtime._core.Tensor, n_fft: int, hop_length: int | None = None, win_length: int | None = None, window: clika_runtime._core.Tensor | None = None, center: bool = True, pad_mode: object = 'reflect', normalized: bool = False, onesided: bool = True) -> clika_runtime._core.Tensor stft(input, n_fft, hop_length=None, win_length=None, window=None, center=True, pad_mode='reflect', normalized=False, onesided=True) -> Tensor The `stft` operator. ## `sub` ```python subsub(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` sub(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor sub(input: clika_runtime._core.ops.Scalar, other: clika_runtime._core.Tensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor sub(input, other, alpha=1.0, *, activation='identity') -> Tensor The `sub` operator. ## `sub_` ```python sub_sub_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor ``` sub_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor, alpha: float = 1.0, *, activation: object | None = 'identity') -> clika_runtime._core.Tensor sub_(self, other, alpha=1.0, *, activation='identity') -> Tensor The `sub_` operator. ## `sum` ```python sumsum(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor ``` sum(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], keepdim: bool = False, *, dtype: clika_runtime._core.DataType = DataType.Undefined) -> clika_runtime._core.Tensor sum(input, dims=[], keepdim=False, *, dtype=Undefined) -> Tensor The `sum` operator. ## `swiglu` ```python swigluswiglu(input: clika_runtime._core.Tensor, alpha: float = 1.0, beta: float = 0.0, limit: float = inf) -> clika_runtime._core.Tensor ``` swiglu(input: clika_runtime._core.Tensor, alpha: float = 1.0, beta: float = 0.0, limit: float = inf) -> clika_runtime._core.Tensor swiglu(input, alpha=1.0, beta=0.0, limit=None) -> Tensor The `swiglu` operator. ## `synchronize_all` ```python synchronize_allsynchronize_all() -> None ``` synchronize_all() -> None synchronize_all() -> None The `synchronize_all` operator. ## `take` ```python taketake(self: clika_runtime._core.Tensor, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` take(self: clika_runtime._core.Tensor, index: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor take(self, index) -> Tensor The `take` operator. ## `take_along_dim` ```python take_along_dimtake_along_dim(input: clika_runtime._core.Tensor, index: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor ``` take_along_dim(input: clika_runtime._core.Tensor, index: clika_runtime._core.Tensor, dim: int | None = None) -> clika_runtime._core.Tensor take_along_dim(input, index, dim=None) -> Tensor The `take_along_dim` operator. ## `tan` ```python tantan(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` tan(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor tan(input) -> Tensor The `tan` operator. ## `tan_` ```python tan_tan_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` tan_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor tan_(self) -> Tensor The `tan_` operator. ## `tanh` ```python tanhtanh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` tanh(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor tanh(input) -> Tensor The `tanh` operator. ## `tanh_` ```python tanh_tanh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` tanh_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor tanh_(self) -> Tensor The `tanh_` operator. ## `tensordot` ```python tensordottensordot(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, dims_a: collections.abc.Sequence[int], dims_b: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor ``` tensordot(input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor, dims_a: collections.abc.Sequence[int], dims_b: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor tensordot(input, other, dims_a, dims_b) -> Tensor The `tensordot` operator. ## `threshold` ```python thresholdthreshold(input: clika_runtime._core.Tensor, threshold: float, value: float) -> clika_runtime._core.Tensor ``` threshold(input: clika_runtime._core.Tensor, threshold: float, value: float) -> clika_runtime._core.Tensor threshold(input, threshold, value) -> Tensor The `threshold` operator. ## `threshold_` ```python threshold_threshold_(self: clika_runtime._core.Tensor, threshold: float, value: float) -> clika_runtime._core.Tensor ``` threshold_(self: clika_runtime._core.Tensor, threshold: float, value: float) -> clika_runtime._core.Tensor threshold_(self, threshold, value) -> Tensor The `threshold_` operator. ## `tile` ```python tiletile(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor ``` tile(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor]) -> clika_runtime._core.Tensor tile(input, dims) -> Tensor The `tile` operator. ## `to` ```python toto(src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` to(src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor to(tensors: collections.abc.Sequence[clika_runtime._core.Tensor], *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> list[clika_runtime._core.Tensor] to(src: clika_runtime._core.Tensor, device_str: str) -> clika_runtime._core.Tensor Overloaded function. 1. ``to(src: clika_runtime._core.Tensor, *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor`` to(src, *, target=None) -> Tensor The `to` operator. 2. ``to(tensors: collections.abc.Sequence[clika_runtime._core.Tensor], *, target: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> list[clika_runtime._core.Tensor]`` to(tensors, *, target=None) -> list[Tensor] The `to` operator. 3. ``to(src: clika_runtime._core.Tensor, device_str: str) -> clika_runtime._core.Tensor`` to(src, device_str) -> Tensor The `to` operator. ## `topk` ```python topktopk(input: clika_runtime._core.Tensor, k: int, dim: int = -1, largest: bool = True, sorted: bool = True) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` topk(input: clika_runtime._core.Tensor, k: int, dim: int = -1, largest: bool = True, sorted: bool = True) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor] topk(input, k, dim=-1, largest=True, sorted=True) -> tuple[Tensor, Tensor] The `topk` operator. ## `trace` ```python tracetrace(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` trace(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor trace(input) -> Tensor The `trace` operator. ## `transpose` ```python transposetranspose(input: clika_runtime._core.Tensor, dim0: int, dim1: int) -> clika_runtime._core.Tensor ``` transpose(input: clika_runtime._core.Tensor, dim0: int, dim1: int) -> clika_runtime._core.Tensor transpose(input, dim0, dim1) -> Tensor The `transpose` operator. ## `tril` ```python triltril(input: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor ``` tril(input: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor tril(input, diagonal=0) -> Tensor The `tril` operator. ## `tril_` ```python tril_tril_(self: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor ``` tril_(self: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor tril_(self, diagonal=0) -> Tensor The `tril_` operator. ## `triu` ```python triutriu(input: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor ``` triu(input: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor triu(input, diagonal=0) -> Tensor The `triu` operator. ## `triu_` ```python triu_triu_(self: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor ``` triu_(self: clika_runtime._core.Tensor, diagonal: int = 0) -> clika_runtime._core.Tensor triu_(self, diagonal=0) -> Tensor The `triu_` operator. ## `trunc` ```python trunctrunc(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` trunc(input: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor trunc(input) -> Tensor The `trunc` operator. ## `trunc_` ```python trunc_trunc_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` trunc_(self: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor trunc_(self) -> Tensor The `trunc_` operator. ## `unflatten` ```python unflattenunflatten(input: clika_runtime._core.Tensor, dim: int, sizes: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor ``` unflatten(input: clika_runtime._core.Tensor, dim: int, sizes: collections.abc.Sequence[int]) -> clika_runtime._core.Tensor unflatten(input, dim, sizes) -> Tensor The `unflatten` operator. ## `unfold` ```python unfoldunfold(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], stride: collections.abc.Sequence[int] = [], mode: object = 'constant', value: float | None = None) -> clika_runtime._core.Tensor ``` unfold(input: clika_runtime._core.Tensor, kernel_size: collections.abc.Sequence[int], dilation: collections.abc.Sequence[int] = [], padding: collections.abc.Sequence[int] = [], stride: collections.abc.Sequence[int] = [], mode: object = 'constant', value: float | None = None) -> clika_runtime._core.Tensor unfold(input, kernel_size, dilation=[], padding=[], stride=[], mode='constant', value=None) -> Tensor The `unfold` operator. ## `uniform_` ```python uniform_uniform_(self: clika_runtime._core.Tensor, low: float = 0.0, high: float = 1.0, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` uniform_(self: clika_runtime._core.Tensor, low: float = 0.0, high: float = 1.0, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor uniform_(self, low=0.0, high=1.0, *, device=None) -> Tensor The `uniform_` operator. ## `unique` ```python uniqueunique(input: clika_runtime._core.Tensor, sorted: bool = True, return_inverse: bool = False, return_counts: bool = False, dim: int | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` unique(input: clika_runtime._core.Tensor, sorted: bool = True, return_inverse: bool = False, return_counts: bool = False, dim: int | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] The `unique` operator. ## `unique_consecutive` ```python unique_consecutiveunique_consecutive(input: clika_runtime._core.Tensor, return_inverse: bool = False, return_counts: bool = False, dim: int | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] ``` unique_consecutive(input: clika_runtime._core.Tensor, return_inverse: bool = False, return_counts: bool = False, dim: int | None = None) -> tuple[clika_runtime._core.Tensor, clika_runtime._core.Tensor, clika_runtime._core.Tensor] unique_consecutive(input, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] The `unique_consecutive` operator. ## `unsqueeze` ```python unsqueezeunsqueeze(input: clika_runtime._core.Tensor, dim: int) -> clika_runtime._core.Tensor ``` unsqueeze(input: clika_runtime._core.Tensor, dim: int) -> clika_runtime._core.Tensor unsqueeze(input, dim) -> Tensor The `unsqueeze` operator. ## `unsqueeze_` ```python unsqueeze_unsqueeze_(self: clika_runtime._core.Tensor, dim: int) -> clika_runtime._core.Tensor ``` unsqueeze_(self: clika_runtime._core.Tensor, dim: int) -> clika_runtime._core.Tensor unsqueeze_(self, dim) -> Tensor The `unsqueeze_` operator. ## `upsample_bicubic2d` ```python upsample_bicubic2dupsample_bicubic2d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor ``` upsample_bicubic2d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor upsample_bicubic2d(input, sizes=[], scale_factors=[], align_corners=False) -> Tensor The `upsample_bicubic2d` operator. ## `upsample_bilinear2d` ```python upsample_bilinear2dupsample_bilinear2d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor ``` upsample_bilinear2d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor upsample_bilinear2d(input, sizes=[], scale_factors=[], align_corners=False) -> Tensor The `upsample_bilinear2d` operator. ## `upsample_linear1d` ```python upsample_linear1dupsample_linear1d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor ``` upsample_linear1d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor upsample_linear1d(input, sizes=[], scale_factors=[], align_corners=False) -> Tensor The `upsample_linear1d` operator. ## `upsample_nearest1d` ```python upsample_nearest1dupsample_nearest1d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = []) -> clika_runtime._core.Tensor ``` upsample_nearest1d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = []) -> clika_runtime._core.Tensor upsample_nearest1d(input, sizes=[], scale_factors=[]) -> Tensor The `upsample_nearest1d` operator. ## `upsample_nearest2d` ```python upsample_nearest2dupsample_nearest2d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = []) -> clika_runtime._core.Tensor ``` upsample_nearest2d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = []) -> clika_runtime._core.Tensor upsample_nearest2d(input, sizes=[], scale_factors=[]) -> Tensor The `upsample_nearest2d` operator. ## `upsample_nearest3d` ```python upsample_nearest3dupsample_nearest3d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = []) -> clika_runtime._core.Tensor ``` upsample_nearest3d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = []) -> clika_runtime._core.Tensor upsample_nearest3d(input, sizes=[], scale_factors=[]) -> Tensor The `upsample_nearest3d` operator. ## `upsample_trilinear3d` ```python upsample_trilinear3dupsample_trilinear3d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor ``` upsample_trilinear3d(input: clika_runtime._core.Tensor, sizes: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], scale_factors: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor] = [], align_corners: bool = False) -> clika_runtime._core.Tensor upsample_trilinear3d(input, sizes=[], scale_factors=[], align_corners=False) -> Tensor The `upsample_trilinear3d` operator. ## `validate_rotary_dim` ```python validate_rotary_dimvalidate_rotary_dim(rotary_dim: int, head_dim: int = -1) -> None ``` validate_rotary_dim(rotary_dim: int, head_dim: int = -1) -> None validate_rotary_dim(rotary_dim, head_dim=-1) -> None The `validate_rotary_dim` operator. ## `var` ```python varvar(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], correction: int = 1, keepdim: bool = False) -> clika_runtime._core.Tensor ``` var(input: clika_runtime._core.Tensor, dims: collections.abc.Sequence[int] = [], correction: int = 1, keepdim: bool = False) -> clika_runtime._core.Tensor var(input, dims=[], correction=1, keepdim=False) -> Tensor The `var` operator. ## `where` ```python wherewhere(condition: clika_runtime._core.Tensor, input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor ``` where(condition: clika_runtime._core.Tensor, input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor where(condition: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor Overloaded function. 1. ``where(condition: clika_runtime._core.Tensor, input: clika_runtime._core.Tensor, other: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` where(condition, input, other) -> Tensor The `where` operator. 2. ``where(condition: clika_runtime._core.Tensor) -> clika_runtime._core.Tensor`` where(condition) -> Tensor The `where` operator. ## `xlog1py` ```python xlog1pyxlog1py(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` xlog1py(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor xlog1py(input, other) -> Tensor The `xlog1py` operator. ## `xlogy` ```python xlogyxlogy(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` xlogy(input: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor xlogy(input, other) -> Tensor The `xlogy` operator. ## `xlogy_` ```python xlogy_xlogy_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor ``` xlogy_(self: clika_runtime._core.Tensor, other: clika_runtime._core.ops.ScalarOrTensor) -> clika_runtime._core.Tensor xlogy_(self, other) -> Tensor The `xlogy_` operator. ## `zero_` ```python zero_zero_(self: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` zero_(self: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor zero_(self, *, device=None) -> Tensor The `zero_` operator. ## `zeros` ```python zeroszeros(shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], dtype: clika_runtime._core.DataType, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...), pinned_for: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` zeros(shape: collections.abc.Sequence[clika_runtime._core.ops.ScalarOrTensor], dtype: clika_runtime._core.DataType, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...), pinned_for: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor zeros(shape, dtype, *, device=None, pinned_for=None) -> Tensor The `zeros` operator. ## `zeros_like` ```python zeros_likezeros_like(reference: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor ``` zeros_like(reference: clika_runtime._core.Tensor, *, device: clika_runtime._core.ops.StreamOrDevice = StreamOrDevice(...)) -> clika_runtime._core.Tensor zeros_like(reference, *, device=None) -> Tensor The `zeros_like` operator.