code: def clear_session():
      """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 = keras.Sequential([
            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.
        keras.backend.clear_session()
        model = keras.Sequential([
            keras.layers.Dense(10) for _ in range(10)])
      ```
  
      Example 2: resetting the layer name generation counter
  
      >>> layers = [keras.layers.Dense(10) for _ in range(10)]
      >>> new_layer = keras.layers.Dense(10)
      >>> print(new_layer.name)
      dense_10
      >>> keras.backend.clear_session()
      >>> new_layer = keras.layers.Dense(10)
      >>> print(new_layer.name)
      dense
      """
      global GLOBAL_STATE_TRACKER
      global GLOBAL_SETTINGS_TRACKER
  
      GLOBAL_STATE_TRACKER = threading.local()
      GLOBAL_SETTINGS_TRACKER = threading.local()
  
      if backend.backend() == "tensorflow":
          from keras.utils.module_utils import tensorflow as tf
  
          tf.compat.v1.reset_default_graph()
          if tf.executing_eagerly():
              # Clear pending nodes in eager executors, kernel caches and
              # step_containers.
              from tensorflow.python.eager import context
  
              context.context().clear_kernel_cache()
      elif backend.backend() == "torch":
          import torch._dynamo as dynamo
  
          # reset's torchdynamo's cache so that  cached guards, compiled fn, etc
          # do not persist between clear_session() calls
          dynamo.reset()
  
imports: import threading
  from keras import backend
  from keras.api_export import keras_export
label_desc: Use image node_id 3 for calling a global function with example usage: clear_session() without return types