Replace Incomplete | None = None in third party stubs (#14063)

stubs/tensorflow/tensorflow/__init__.pyi

@@ -161,7 +161,7 @@ class Variable(Tensor, metaclass=_VariableMetaclass):
         name: str | None = None,
         # Real type is VariableDef protobuf type. Can be added after adding script
         # to generate tensorflow protobuf stubs with mypy-protobuf.
-        variable_def: Incomplete | None = None,
+        variable_def=None,
         dtype: DTypeLike | None = None,
         import_scope: str | None = None,
         constraint: Callable[[Tensor], Tensor] | None = None,
@@ -203,7 +203,7 @@ class Operation:
         control_inputs: Iterable[Tensor | Operation] | None = None,
         input_types: Iterable[DType] | None = None,
         original_op: Operation | None = None,
-        op_def: Incomplete | None = None,
+        op_def=None,
     ) -> None: ...
     @property
     def inputs(self) -> list[Tensor]: ...

stubs/tensorflow/tensorflow/data/__init__.pyi

@@ -187,7 +187,7 @@ class Dataset(ABC, Generic[_T1_co]):
         path: str,
         compression: _CompressionTypes = None,
         shard_func: Callable[[_T1_co], int] | None = None,
-        checkpoint_args: Incomplete | None = None,
+        checkpoint_args=None,
     ) -> None: ...
     def scan(
         self, initial_state: _T2, scan_func: Callable[[_T2, _T1_co], tuple[_T2, _T3]], name: str | None = None

stubs/tensorflow/tensorflow/keras/losses.pyi

@@ -1,4 +1,3 @@
-from _typeshed import Incomplete
 from abc import ABC, abstractmethod
 from collections.abc import Callable
 from typing import Any, Final, Literal, TypeVar, overload
@@ -14,9 +13,7 @@ from tensorflow.keras.metrics import (
 class Loss(ABC):
     reduction: _ReductionValues
     name: str | None
-    def __init__(
-        self, name: str | None = None, reduction: _ReductionValues = "sum_over_batch_size", dtype: Incomplete | None = None
-    ) -> None: ...
+    def __init__(self, name: str | None = None, reduction: _ReductionValues = "sum_over_batch_size", dtype=None) -> None: ...
     @abstractmethod
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
     @classmethod
@@ -34,7 +31,7 @@ class BinaryCrossentropy(Loss):
         axis: int = -1,
         reduction: _ReductionValues = "sum_over_batch_size",
         name: str | None = "binary_crossentropy",
-        dtype: Incomplete | None = None,
+        dtype=None,
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
@@ -49,7 +46,7 @@ class BinaryFocalCrossentropy(Loss):
         axis: int = -1,
         reduction: _ReductionValues = "sum_over_batch_size",
         name: str | None = "binary_focal_crossentropy",
-        dtype: Incomplete | None = None,
+        dtype=None,
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
@@ -61,16 +58,13 @@ class CategoricalCrossentropy(Loss):
         axis: int = -1,
         reduction: _ReductionValues = "sum_over_batch_size",
         name: str | None = "categorical_crossentropy",
-        dtype: Incomplete | None = None,
+        dtype=None,
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
 class CategoricalHinge(Loss):
     def __init__(
-        self,
-        reduction: _ReductionValues = "sum_over_batch_size",
-        name: str | None = "categorical_hinge",
-        dtype: Incomplete | None = None,
+        self, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "categorical_hinge", dtype=None
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
@@ -80,81 +74,58 @@ class CosineSimilarity(Loss):
         axis: int = -1,
         reduction: _ReductionValues = "sum_over_batch_size",
         name: str | None = "cosine_similarity",
-        dtype: Incomplete | None = None,
+        dtype=None,
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
 class Hinge(Loss):
-    def __init__(
-        self, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "hinge", dtype: Incomplete | None = None
-    ) -> None: ...
+    def __init__(self, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "hinge", dtype=None) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
 class Huber(Loss):
     def __init__(
-        self,
-        delta: float = 1.0,
-        reduction: _ReductionValues = "sum_over_batch_size",
-        name: str | None = "huber_loss",
-        dtype: Incomplete | None = None,
+        self, delta: float = 1.0, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "huber_loss", dtype=None
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
 class KLDivergence(Loss):
     def __init__(
-        self,
-        reduction: _ReductionValues = "sum_over_batch_size",
-        name: str | None = "kl_divergence",
-        dtype: Incomplete | None = None,
+        self, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "kl_divergence", dtype=None
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
 class LogCosh(Loss):
     def __init__(
-        self, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "log_cosh", dtype: Incomplete | None = None
+        self, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "log_cosh", dtype=None
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
 class MeanAbsoluteError(Loss):
     def __init__(
-        self,
-        reduction: _ReductionValues = "sum_over_batch_size",
-        name: str | None = "mean_absolute_error",
-        dtype: Incomplete | None = None,
+        self, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "mean_absolute_error", dtype=None
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
 class MeanAbsolutePercentageError(Loss):
     def __init__(
-        self,
-        reduction: _ReductionValues = "sum_over_batch_size",
-        name: str | None = "mean_absolute_percentage_error",
-        dtype: Incomplete | None = None,
+        self, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "mean_absolute_percentage_error", dtype=None
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
 class MeanSquaredError(Loss):
     def __init__(
-        self,
-        reduction: _ReductionValues = "sum_over_batch_size",
-        name: str | None = "mean_squared_error",
-        dtype: Incomplete | None = None,
+        self, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "mean_squared_error", dtype=None
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
 class MeanSquaredLogarithmicError(Loss):
     def __init__(
-        self,
-        reduction: _ReductionValues = "sum_over_batch_size",
-        name: str | None = "mean_squared_logarithmic_error",
-        dtype: Incomplete | None = None,
+        self, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "mean_squared_logarithmic_error", dtype=None
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
 class Poisson(Loss):
-    def __init__(
-        self, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "poisson", dtype: Incomplete | None = None
-    ) -> None: ...
+    def __init__(self, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "poisson", dtype=None) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
 class SparseCategoricalCrossentropy(Loss):
@@ -164,16 +135,13 @@ class SparseCategoricalCrossentropy(Loss):
         ignore_class: int | None = None,
         reduction: _ReductionValues = "sum_over_batch_size",
         name: str = "sparse_categorical_crossentropy",
-        dtype: Incomplete | None = None,
+        dtype=None,
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 
 class SquaredHinge(Loss):
     def __init__(
-        self,
-        reduction: _ReductionValues = "sum_over_batch_size",
-        name: str | None = "squared_hinge",
-        dtype: Incomplete | None = None,
+        self, reduction: _ReductionValues = "sum_over_batch_size", name: str | None = "squared_hinge", dtype=None
     ) -> None: ...
     def call(self, y_true: Tensor, y_pred: Tensor) -> Tensor: ...
 

stubs/tensorflow/tensorflow/keras/models.pyi

@@ -22,9 +22,7 @@ class Model(Layer[_InputT_contra, _OutputT_co]):
     optimizer: Optimizer | None
     # This is actually TensorFlowTrainer.loss
     @deprecated("Instead, use `model.compute_loss(x, y, y_pred, sample_weight)`.")
-    def loss(
-        self, y: TensorCompatible | None, y_pred: TensorCompatible | None, sample_weight: Incomplete | None = None
-    ) -> tf.Tensor | None: ...
+    def loss(self, y: TensorCompatible | None, y_pred: TensorCompatible | None, sample_weight=None) -> tf.Tensor | None: ...
     stop_training: bool
 
     def __new__(cls, *args: Any, **kwargs: Any) -> Model[_InputT_contra, _OutputT_co]: ...
@@ -67,11 +65,11 @@ class Model(Layer[_InputT_contra, _OutputT_co]):
         x: TensorCompatible | None = None,
         y: TensorCompatible | None = None,
         y_pred: TensorCompatible | None = None,
-        sample_weight: Incomplete | None = None,
+        sample_weight=None,
         training: bool = True,
     ) -> tf.Tensor | None: ...
     def compute_metrics(
-        self, x: TensorCompatible, y: TensorCompatible, y_pred: TensorCompatible, sample_weight: Incomplete | None = None
+        self, x: TensorCompatible, y: TensorCompatible, y_pred: TensorCompatible, sample_weight=None
     ) -> dict[str, float]: ...
     def get_metrics_result(self) -> dict[str, float]: ...
     def make_train_function(self, force: bool = False) -> Callable[[tf.data.Iterator[Incomplete]], dict[str, float]]: ...
@@ -146,7 +144,7 @@ class Model(Layer[_InputT_contra, _OutputT_co]):
     def load_weights(self, filepath: str | Path, skip_mismatch: bool = False, *, by_name: bool = False) -> None: ...
     def get_config(self) -> dict[str, Any]: ...
     @classmethod
-    def from_config(cls, config: dict[str, Any], custom_objects: Incomplete | None = None) -> Self: ...
+    def from_config(cls, config: dict[str, Any], custom_objects=None) -> Self: ...
     def to_json(self, **kwargs: Any) -> str: ...
     @property
     def weights(self) -> list[Variable]: ...

stubs/tensorflow/tensorflow/saved_model/__init__.pyi

@@ -71,8 +71,8 @@ class SaveOptions:
         experimental_custom_gradients: bool = True,
         experimental_image_format: bool = False,
         experimental_skip_saver: bool = False,
-        experimental_sharding_callback: Incomplete | None = None,
-        extra_tags: Incomplete | None = None,
+        experimental_sharding_callback=None,
+        extra_tags=None,
     ) -> None: ...
 
 def contains_saved_model(export_dir: str | Path) -> bool: ...

stubs/tensorflow/tensorflow/summary.pyi

@@ -1,5 +1,4 @@
 import abc
-from _typeshed import Incomplete
 from collections.abc import Callable, Generator
 from contextlib import AbstractContextManager, contextmanager
 from typing import Literal
@@ -56,6 +55,4 @@ def text(name: str, data: str | tf.Tensor, step: int | tf.Tensor | None = None,
 def trace_export(name: str, step: int | tf.Tensor | None = None, profiler_outdir: str | None = None) -> None: ...
 def trace_off() -> None: ...
 def trace_on(graph: bool = True, profiler: bool = False, profiler_outdir: str | None = None) -> None: ...
-def write(
-    tag: str, tensor: tf.Tensor, step: int | tf.Tensor | None = None, metadata: Incomplete | None = None, name: str | None = None
-) -> bool: ...
+def write(tag: str, tensor: tf.Tensor, step: int | tf.Tensor | None = None, metadata=None, name: str | None = None) -> bool: ...

stubs/tensorflow/tensorflow/train/__init__.pyi

@@ -1,4 +1,3 @@
-from _typeshed import Incomplete
 from collections.abc import Callable
 from typing import Any, TypeVar
 from typing_extensions import Self
@@ -31,7 +30,7 @@ class CheckpointOptions:
         experimental_write_callbacks: None | list[Callable[[str], object] | Callable[[], object]] = None,
         enable_async: bool = False,
         experimental_skip_slot_variables: bool = False,
-        experimental_sharding_callback: Incomplete | None = None,
+        experimental_sharding_callback=None,
     ) -> None: ...
 
 _T = TypeVar("_T", bound=list[str] | tuple[str] | dict[int, str])