parameters: ()
docstring: 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)
    ```
docstring_summary: Use Keras-style variable management.
docstring_tokens: Use Keras - style variable management .
function: 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
function_tokens: def set_keras_style ( ) : global _KERAS_STYLE_SCOPE _KERAS_STYLE_SCOPE = True