PR #17956: [keras/engine] Standardise docstring usage of "Default to"

Imported from GitHub PR #17956

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

--
9ad7371 by Samuel Marks <[email protected]>:

[keras/engine/base_layer.py,keras/engine/base_layer_utils.py,keras/engine/base_layer_v1.py,keras/engine/base_preprocessing_layer.py,keras/engine/data_adapter.py,keras/engine/functional.py,keras/engine/input_layer.py,keras/engine/training.py,keras/engine/training_v1.py] Standardise docstring usage of "Default to"

Merging this change closes #17956

FUTURE_COPYBARA_INTEGRATE_REVIEW=#17956 from SamuelMarks:keras.engine-defaults-to 9ad7371
PiperOrigin-RevId: 530510437

Author: SamuelMarks

keras/engine/base_layer_v1.py

@@ -237,7 +237,7 @@ def __init__(
 
         # Whether the layer will track any layers that are set as attribute on
         # itself as sub-layers, the weights from the sub-layers will be included
-        # in the parent layer's variables() as well.  Default to True, which
+        # in the parent layer's variables() as well.  Defaults to `True`, which
         # means auto tracking is turned on. Certain subclass might want to turn
         # it off, like the Sequential model.
         self._auto_track_sub_layers = True

keras/engine/base_preprocessing_layer.py

@@ -140,14 +140,14 @@ def compile(self, run_eagerly=None, steps_per_execution=None):
         """Configures the layer for `adapt`.
 
         Arguments:
-          run_eagerly: Bool. Defaults to `False`. If `True`, this `Model`'s
+          run_eagerly: Bool. If `True`, this `Model`'s
             logic will not be wrapped in a `tf.function`. Recommended to leave
             this as `None` unless your `Model` cannot be run inside a
-            `tf.function`.
-          steps_per_execution: Int. Defaults to 1. The number of batches to run
+            `tf.function`. Defaults to `False`.
+          steps_per_execution: Int. The number of batches to run
             during each `tf.function` call. Running multiple batches inside a
             single `tf.function` call can greatly improve performance on TPUs or
-            small models with a large Python overhead.
+            small models with a large Python overhead. Defaults to `1`.
         """
         if steps_per_execution is None:
             steps_per_execution = 1