code: def set_keras_style():
      """Use Keras-style variable management.
  
      All tf.layers and tf RNN cells created after keras style ha been enabled
      use Keras-style variable management.  Creating such layers with a
      scope= argument is disallowed, and reuse=True is disallowed.
  
      The purpose of this function is to allow users of existing layers to
      slowly transition to Keras layers API without breaking existing
      functionality.
  
      For more details, see the documentation for `keras_style_scope`.
  
      Note, once keras style has been set, it is set globally for the entire
      program and cannot be unset.
  
      Example:
  
      ```python
      set_keras_style()
  
      model_1 = RNNModel(name="model_1")
      model_2 = RNNModel(name="model_2")
  
      # model_1 and model_2 are guaranteed to create their own variables.
      output_1, next_state_1 = model_1(input, state)
      output_2, next_state_2 = model_2(input, state)
  
      assert len(model_1.weights) > 0
      assert len(model_2.weights) > 0
      assert(model_1.weights != model_2.weights)
      ```
      """
      global _KERAS_STYLE_SCOPE
      _KERAS_STYLE_SCOPE = True
  
imports: import copy
  import warnings
  from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.keras import backend
  from tensorflow.python.keras.engine import base_layer
  from tensorflow.python.keras.engine import base_layer_utils
  from tensorflow.python.keras.legacy_tf_layers import variable_scope_shim
  from tensorflow.python.keras.mixed_precision import policy
  from tensorflow.python.keras.utils import tf_contextlib
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.trackable import base
  from tensorflow.python.util import nest
label_desc: Use image node_id 2 for calling a global function with example usage: set_keras_style() without return types