message: Update description of `clear_session()`.
diff: @@ -240,14 +240,33 @@ def reset_uids():
  @keras_export('keras.backend.clear_session')
  def clear_session():
  - """Destroys the current TF graph and session, and creates a new one.
  + """Resets all state generated by Keras.
  +
  + Keras manages a global state, which it uses to implement the Functional
  + model-building API and to uniquify autogenerated layer names.
  +
  + If you are creating many models in a loop, this global state will consume
  + an increasing amount of memory over time, and you may want to clear it.
  + Calling `clear_session()` releases the global state: this helps avoid clutter
  + from old models and layers, especially when memory is limited.
  +
  + Example 1: calling `clear_session()` when creating models in a loop
  +
  + ```python
  + for _ in range(100):
  + # Without `clear_session()`, each iteration of this loop will
  + # slightly increase the size of the global state managed by Keras
  + model = tf.keras.Sequential([tf.keras.layers.Dense(10) for _ in range(10)])
  +
  + for _ in range(100):
  + # With `clear_session()` called at the beginning,
  + # Keras starts with a blank state at each iteration
  + # and memory consumption is constant over time.
  + tf.keras.backend.clear_session()
  + model = tf.keras.Sequential([tf.keras.layers.Dense(10) for _ in range(10)])
  + ```
  - Calling clear_session() releases the global graph state that Keras is
  - holding on to; resets the counters used for naming layers and
  - variables in Keras; and resets the learning phase. This helps avoid clutter
  - from old models and layers, especially when memory is limited, and a
  - common use-case for clear_session is releasing memory when building models
  - and layers in a loop.
  + Example 2: resetting the layer name generation counter
  >>> import tensorflow as tf
  >>> layers = [tf.keras.layers.Dense(10) for _ in range(10)]
  @@ -261,8 +280,6 @@ def clear_session():
  >>> new_layer = tf.keras.layers.Dense(10)
  >>> print(new_layer.name)
  dense
  - >>> print(tf.keras.backend.learning_phase())
  - 0
  """
  global _SESSION
  global _GRAPH_LEARNING_PHASES # pylint: disable=global-variable-not-assigned
  