parameters: (tensors, ignore_call_context=False)
return_statement: return uses_keras_history(tensors)
docstring: Check if any Tensors need to be wrapped in TensorFlowOpLayers.
  
    This will never return True inside a sublayer, because sublayers
    do not need to create Keras History. Otherwise, this returns True
    if one or more of `tensors` originates from a `keras.Input` and
    does not have `_keras_history` set.
  
    Arguments:
      tensors: An arbitrary nested structure of Tensors.
      ignore_call_context: Whether to ignore the check of if currently
        outside of a `call` context. This is `True` when creating
        KerasHistory inside `Node`, where we always know that Tensors
        are being used with the Functional API.
  
    Returns:
      Bool, whether at least one Tensor needs to be wrapped.
docstring_summary: Check if any Tensors need to be wrapped in TensorFlowOpLayers.
docstring_tokens: Check if any Tensors need to be wrapped in TensorFlowOpLayers .
function: def needs_keras_history(tensors, ignore_call_context=False):
    """Check if any Tensors need to be wrapped in TensorFlowOpLayers.
  
    This will never return True inside a sublayer, because sublayers
    do not need to create Keras History. Otherwise, this returns True
    if one or more of `tensors` originates from a `keras.Input` and
    does not have `_keras_history` set.
  
    Arguments:
      tensors: An arbitrary nested structure of Tensors.
      ignore_call_context: Whether to ignore the check of if currently
        outside of a `call` context. This is `True` when creating
        KerasHistory inside `Node`, where we always know that Tensors
        are being used with the Functional API.
  
    Returns:
      Bool, whether at least one Tensor needs to be wrapped.
    """
    input_tensors = nest.flatten(tensors)
    if call_context().in_call and not ignore_call_context:
      return False
    if all(
        getattr(tensor, '_keras_history', None) is not None
        for tensor in input_tensors):
      # KerasHistory already set.
      return False
    return uses_keras_history(tensors)
function_tokens: def needs_keras_history ( tensors , ignore_call_context = False ) : input_tensors = nest . flatten ( tensors ) if call_context ( ) . in_call and not ignore_call_context : return False if all ( getattr ( tensor , '_keras_history' , None ) is not None for tensor in input_tensors ) : # KerasHistory already set. return False return uses_keras_history ( tensors )