PR #17971: [keras/legacy_tf_layers] Standardise docstring usage of "D…

…efault to"

Imported from GitHub PR #17971

This is one of many PRs. Discussion + request to split into multiple PRs @ #17748
Copybara import of the project:

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5331dac by Samuel Marks <[email protected]>:

[keras/legacy_tf_layers/base.py,keras/legacy_tf_layers/migration_utils.py,keras/legacy_tf_layers/variable_scope_shim.py] Standardise docstring usage of "Default to"

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1801651 by Samuel Marks <[email protected]>:

[keras/legacy_tf_layers/migration_utils.py] Move docstring from method to class and document `seed`

Merging this change closes #17971

FUTURE_COPYBARA_INTEGRATE_REVIEW=#17971 from SamuelMarks:keras.legacy_tf_layers-defaults-to 1801651
PiperOrigin-RevId: 535274600

keras/legacy_tf_layers/base.py

@@ -365,8 +365,8 @@ def add_weight(
             or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
             Note, if the current variable scope is marked as non-trainable
             then this parameter is ignored and any added variables are also
-            marked as non-trainable. `trainable` defaults to `True` unless
-            `synchronization` is set to `ON_READ`.
+            marked as non-trainable. `trainable` becomes `True` unless
+            `synchronization` is set to `ON_READ`. Defaults to `True`.
           constraint: constraint instance (callable).
           use_resource: Whether to use `ResourceVariable`.
           synchronization: Indicates when a distributed a variable will be

keras/legacy_tf_layers/migration_utils.py

@@ -43,11 +43,14 @@ class DeterministicRandomTestTool(object):
     This applies both to the stateful random operations used for creating and
     initializing variables, and to the stateful random operations used in
     computation (such as for dropout layers).
+
+    Args:
+      mode: Set mode to 'constant' or 'num_random_ops'. Defaults to
+        'constant'.
+      seed: The random seed to use.
     """
 
     def __init__(self, seed: int = 42, mode="constant"):
-        """Set mode to 'constant' or 'num_random_ops'. Defaults to
-        'constant'."""
         if mode not in {"constant", "num_random_ops"}:
             raise ValueError(
                 "Mode arg must be 'constant' or 'num_random_ops'. "

keras/legacy_tf_layers/variable_scope_shim.py

@@ -215,7 +215,7 @@ def get_variable(
         Args:
           name: The name of the new or existing variable.
           shape: Shape of the new or existing variable.
-          dtype: Type of the new or existing variable (defaults to `DT_FLOAT`).
+          dtype: Type of the new or existing variable. Defaults to `DT_FLOAT`.
           initializer: Initializer for the variable.
           regularizer: A (Tensor -> Tensor or None) function; the result of
             applying it on a newly created variable will be added to the
@@ -226,16 +226,16 @@ def get_variable(
             always forced to be False.
           trainable: If `True` also add the variable to the graph collection
             `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). `trainable`
-            defaults to `True`, unless `synchronization` is set to `ON_READ`, in
-            which case it defaults to `False`.
+            becomes `True`, unless `synchronization` is set to `ON_READ`, in
+            which case it becomes `False`. Defaults to `True`.
           collections: List of graph collections keys to add the `Variable` to.
             Defaults to `[GraphKeys.GLOBAL_VARIABLES]` (see `tf.Variable`).
           caching_device: Optional device string or function describing where
-            the Variable should be cached for reading.  Defaults to the
+            the Variable should be cached for reading. `None` to use the
             Variable's device.  If not `None`, caches on another device.
             Typical use is to cache on the device where the Ops using the
             `Variable` reside, to deduplicate copying through `Switch` and other
-            conditional statements.
+            conditional statements. Defaults to `None`.
           partitioner: Optional callable that accepts a fully defined
             `TensorShape` and dtype of the `Variable` to be created, and returns
             a list of partitions for each axis (currently only one axis can be
@@ -245,8 +245,9 @@ def get_variable(
             initial_value must be known.
           use_resource: If False, creates a regular Variable. If True, creates
             instead an experimental ResourceVariable which has well-defined
-            semantics. Defaults to False (will later change to True). When eager
-            execution is enabled this argument is always forced to be true.
+            semantics. When starting off as False it will later change to True.
+            When eager execution is enabled this argument always True.
+            Defaults to `False`.
           custom_getter: Callable that takes as a first argument the true
             getter, and allows overwriting the internal get_variable method. The
             signature of `custom_getter` should match that of this method, but