tensorflow: add tensorflow.keras.activations members (#11444)

Co-authored-by: Jelle Zijlstra <jelle.zijlstra@gmail.com>

stubs/tensorflow/tensorflow/_aliases.pyi

@@ -34,6 +34,7 @@ IntDataSequence: TypeAlias = Sequence[int] | Sequence[IntDataSequence]
 StrDataSequence: TypeAlias = Sequence[str] | Sequence[StrDataSequence]
 ScalarTensorCompatible: TypeAlias = tf.Tensor | str | float | np.ndarray[Any, Any] | np.number[Any]
 UIntTensorCompatible: TypeAlias = tf.Tensor | int | UIntArray
+FloatTensorCompatible: TypeAlias = tf.Tensor | int | IntArray | float | FloatArray | np.number[Any]
 StringTensorCompatible: TypeAlias = tf.Tensor | str | npt.NDArray[np.str_] | Sequence[StringTensorCompatible]
 
 TensorCompatible: TypeAlias = ScalarTensorCompatible | Sequence[TensorCompatible]

stubs/tensorflow/tensorflow/keras/activations.pyi

@@ -1,12 +1,37 @@
-from _typeshed import Incomplete
 from collections.abc import Callable
 from typing import Any
 from typing_extensions import TypeAlias
 
+import numpy as np
 from tensorflow import Tensor
+from tensorflow._aliases import FloatArray, FloatDataSequence, FloatTensorCompatible, Integer
 
 # The implementation uses isinstance so it must be dict and not any Mapping.
 _Activation: TypeAlias = str | None | Callable[[Tensor], Tensor] | dict[str, Any]
+# Ints are not allowed.
+_ActivationInput: TypeAlias = Tensor | FloatDataSequence | FloatArray | np.number[Any] | float
 
+def deserialize(
+    name: str, custom_objects: dict[str, Callable[..., Any]] | None = None, use_legacy_format: bool = False
+) -> Callable[..., Any]: ...
+def elu(x: _ActivationInput, alpha: FloatTensorCompatible | FloatDataSequence = 1.0) -> Tensor: ...
+def exponential(x: _ActivationInput) -> Tensor: ...
+def gelu(x: _ActivationInput, approximate: bool = False) -> Tensor: ...
 def get(identifier: _Activation) -> Callable[[Tensor], Tensor]: ...
-def __getattr__(name: str) -> Incomplete: ...
+def hard_sigmoid(x: _ActivationInput) -> Tensor: ...
+def linear(x: _ActivationInput) -> Tensor: ...
+def mish(x: _ActivationInput) -> Tensor: ...
+def relu(
+    x: _ActivationInput,
+    alpha: FloatTensorCompatible = 0.0,
+    max_value: FloatTensorCompatible | FloatDataSequence | None = None,
+    threshold: FloatTensorCompatible | FloatDataSequence = 0.0,
+) -> Tensor: ...
+def selu(x: _ActivationInput) -> Tensor: ...
+def serialize(activation: Callable[..., Any], use_legacy_format: bool = False) -> str | dict[str, Any]: ...
+def sigmoid(x: _ActivationInput) -> Tensor: ...
+def softmax(x: Tensor, axis: Integer = -1) -> Tensor: ...
+def softplus(x: _ActivationInput) -> Tensor: ...
+def softsign(x: _ActivationInput) -> Tensor: ...
+def swish(x: _ActivationInput) -> Tensor: ...
+def tanh(x: _ActivationInput) -> Tensor: ...