class_docstr: Base class for Preprocessing Layers.
  
  **Don't use this class directly: it's an abstract base class!** You may
  be looking for one of the many built-in
  [preprocessing layers](https://keras.io/guides/preprocessing_layers/)
  instead.
  
  Preprocessing layers are layers whose state gets computed before model
  training starts. They do not get updated during training.
  Most preprocessing layers implement an `adapt()` method for state computation.
  
  The `PreprocessingLayer` class is the base class you would subclass to
  implement your own preprocessing layers.
  
  Attributes:
    streaming: Whether a layer can be adapted multiple times without resetting
      the state of the layer.
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docstr: Configures the layer for `adapt`.
  
  Arguments:
    run_eagerly: Bool. Defaults to `False`. 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
        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.
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code: 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 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
            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.
      """
      if steps_per_execution is None:
          steps_per_execution = 1
      self._configure_steps_per_execution(steps_per_execution)
  
      if run_eagerly is None:
          run_eagerly = self.dynamic
      self._run_eagerly = run_eagerly
  
      self._is_compiled = True
  
imports: import abc
  import collections
  import numpy
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor
  from tensorflow.python.keras import backend
  from tensorflow.python.keras.engine import data_adapter
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.keras.utils import version_utils
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.trackable import base
label_desc: Use image node_id 9 for calling the PreprocessingLayer obj's underlying member method code with example usage: obj.compile(run_eagerly, steps_per_execution) without return types