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venv/lib/python3.12/site-packages/jax/nn/__init__.pyi

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+# Copyright 2025 The JAX Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, List, Literal, overload, Sequence
+
+from jax._src.core import AxisName
+from jax._src.cudnn.scaled_matmul_stablehlo import BlockScaleConfig
+from jax._src.lax.lax import DotDimensionNumbers
+from jax._src.typing import Array, ArrayLike, DTypeLike
+
+from jax.nn import initializers as initializers
+
+Axis = int | Sequence[int] | None
+
+
+def celu(x: ArrayLike, alpha: ArrayLike = ...) -> Array: ...
+@overload
+def dot_product_attention(
+    query: ArrayLike,
+    key: ArrayLike,
+    value: ArrayLike,
+    bias: ArrayLike | None = ...,
+    mask: ArrayLike | None = ...,
+    *,
+    scale: float | None = ...,
+    is_causal: bool = ...,
+    query_seq_lengths: ArrayLike | None = ...,
+    key_value_seq_lengths: ArrayLike | None = ...,
+    local_window_size: int | tuple[int, int] | None = ...,
+    implementation: Literal['xla', 'cudnn'] | None = ...,
+    return_residual: Literal[False] = ...,
+  ) -> Array: ...
+@overload
+def dot_product_attention(
+    query: ArrayLike,
+    key: ArrayLike,
+    value: ArrayLike,
+    bias: ArrayLike | None = ...,
+    mask: ArrayLike | None = ...,
+    *,
+    scale: float | None = ...,
+    is_causal: bool = ...,
+    query_seq_lengths: ArrayLike | None = ...,
+    key_value_seq_lengths: ArrayLike | None = ...,
+    local_window_size: int | tuple[int, int] | None = ...,
+    implementation: Literal['xla', 'cudnn'] | None = ...,
+    return_residual: Literal[True] = ...,
+  ) -> tuple[Array, Array]: ...
+def elu(x: ArrayLike, alpha: ArrayLike = ...) -> Array: ...
+def gelu(x: ArrayLike, approximate: bool = ...) -> Array: ...
+def get_scaled_dot_general_config(
+    mode: Literal['nvfp4', 'mxfp8'],
+    global_scale: Array | None = ...,
+  ) -> BlockScaleConfig: ...
+def glu(x: ArrayLike, axis: int = ...) -> Array: ...
+def hard_sigmoid(x: ArrayLike) -> Array: ...
+def hard_silu(x: ArrayLike) -> Array: ...
+def hard_swish(x: ArrayLike) -> Array: ...
+def hard_tanh(x: ArrayLike) -> Array: ...
+def identity(x: ArrayLike) -> Array: ...
+def leaky_relu(x: ArrayLike, negative_slope: ArrayLike = ...) -> Array: ...
+def log_sigmoid(x: ArrayLike) -> Array: ...
+def log_softmax(
+    x: ArrayLike,
+    axis: Axis = ...,
+    where: ArrayLike | None = ...,
+  ) -> Array: ...
+def logmeanexp(
+    x: ArrayLike,
+    axis: Axis = None,
+    where: ArrayLike | None = None,
+    keepdims: bool = False,
+) -> Array: ...
+@overload
+def logsumexp(
+    a: ArrayLike,
+    axis: Axis = ...,
+    b: ArrayLike | None = ...,
+    keepdims: bool = ...,
+    return_sign: Literal[False] = ...,
+    where: ArrayLike | None = ...,
+  ) -> Array: ...
+@overload
+def logsumexp(
+    a: ArrayLike,
+    axis: Axis = ...,
+    b: ArrayLike | None = ...,
+    keepdims: bool = ...,
+    *,
+    return_sign: Literal[True],
+    where: ArrayLike | None = ...,
+  ) -> tuple[Array, Array]: ...
+@overload
+def logsumexp(
+    a: ArrayLike,
+    axis: Axis = ...,
+    b: ArrayLike | None = ...,
+    keepdims: bool = ...,
+    return_sign: bool = ...,
+    where: ArrayLike | None = ...,
+  ) -> Array | tuple[Array, Array]: ...
+def mish(x: ArrayLike) -> Array: ...
+def one_hot(
+    x: Any,
+    num_classes: int,
+    *,
+    dtype: Any = ...,
+    axis: int | AxisName = ...
+  ) -> Array: ...
+def relu(x: ArrayLike) -> Array: ...
+def relu6(x: ArrayLike) -> Array: ...
+def scaled_dot_general(
+    lhs: ArrayLike, rhs: ArrayLike,
+    dimension_numbers: DotDimensionNumbers,
+    preferred_element_type: DTypeLike = ...,
+    configs: List[BlockScaleConfig] | None = ...,
+    implementation: Literal['cudnn'] | None = ...,
+  ) -> Array: ...
+def scaled_matmul(
+    lhs: Array,
+    rhs: Array,
+    lhs_scales: Array,
+    rhs_scales: Array,
+    preferred_element_type: DTypeLike = ...,
+  ) -> Array: ...
+def selu(x: ArrayLike) -> Array: ...
+def sigmoid(x: ArrayLike) -> Array: ...
+def silu(x: ArrayLike) -> Array: ...
+def soft_sign(x: ArrayLike) -> Array: ...
+def softmax(
+    x: ArrayLike,
+    axis: Axis = ...,
+    where: ArrayLike | None = ...
+  ) -> Array: ...
+def softplus(x: ArrayLike) -> Array: ...
+def sparse_plus(x: ArrayLike) -> Array: ...
+def sparse_sigmoid(x: ArrayLike) -> Array: ...
+def squareplus(x: ArrayLike, b: ArrayLike = ...) -> Array: ...
+def standardize(
+    x: ArrayLike,
+    axis: Axis = ...,
+    mean: ArrayLike | None = ...,
+    variance: ArrayLike | None = ...,
+    epsilon: ArrayLike = ...,
+    where: ArrayLike | None = ...
+  ) -> Array: ...
+def swish(x: ArrayLike) -> Array: ...
+def tanh(x: ArrayLike, /) -> Array: ...
+def log1mexp(x: ArrayLike) -> Array: ...