parameters: (data, frame_name, is_constant=False, parallel_iterations=10,
             use_ref=True, name=None)
docstring: Creates or finds a child frame, and makes `data` available to it.
  
    The unique `frame_name` is used by the `Executor` to identify frames. If
    `is_constant` is true, `data` is a constant in the child frame; otherwise
    it may be changed in the child frame. At most `parallel_iterations`
    iterations are run in parallel in the child frame.
  
    Args:
      data: The tensor to be made available to the child frame.
      frame_name: The name of the child frame.
      is_constant: If true, the output is constant within the child frame.
      parallel_iterations: The number of iterations allowed to run in parallel.
      use_ref: If true, use ref_enter if data is of ref type.
      name: A name for this operation (optional).
  
    Returns:
      The same tensor as `data`.
docstring_summary: Creates or finds a child frame, and makes `data` available to it.
docstring_tokens: Creates or finds a child frame and makes data available to it .
function: def _Enter(data, frame_name, is_constant=False, parallel_iterations=10,
             use_ref=True, name=None):
    """Creates or finds a child frame, and makes `data` available to it.
  
    The unique `frame_name` is used by the `Executor` to identify frames. If
    `is_constant` is true, `data` is a constant in the child frame; otherwise
    it may be changed in the child frame. At most `parallel_iterations`
    iterations are run in parallel in the child frame.
  
    Args:
      data: The tensor to be made available to the child frame.
      frame_name: The name of the child frame.
      is_constant: If true, the output is constant within the child frame.
      parallel_iterations: The number of iterations allowed to run in parallel.
      use_ref: If true, use ref_enter if data is of ref type.
      name: A name for this operation (optional).
  
    Returns:
      The same tensor as `data`.
    """
    data = ops.convert_to_tensor_or_indexed_slices(data, as_ref=True)
    if isinstance(data, ops.Tensor):
      if data.dtype.is_ref_dtype and use_ref:
        return ref_enter(data, frame_name, is_constant, parallel_iterations,
                         name=name)
      else:
        return enter(data, frame_name, is_constant, parallel_iterations,
                     name=name)
    else:
      if not isinstance(data, (ops.IndexedSlices, ops.SparseTensor)):
        raise TypeError("Type %s not supported" % type(data))
      values = _Enter(data.values, frame_name, is_constant,
                      parallel_iterations, name=name)
      indices = enter(data.indices, frame_name, is_constant,
                      parallel_iterations, name="indices")
      if isinstance(data, ops.IndexedSlices):
        dense_shape = data.dense_shape
        if dense_shape is not None:
          dense_shape = enter(dense_shape, frame_name, is_constant,
                              parallel_iterations, name="dense_shape")
        return ops.IndexedSlices(values, indices, dense_shape)
      else:
        dense_shape = enter(data.shape, frame_name, is_constant,
                            parallel_iterations, name="dense_shape")
        return ops.SparseTensor(indices, values, dense_shape)
function_tokens: def _Enter ( data , frame_name , is_constant = False , parallel_iterations = 10 , use_ref = True , name = None ) : data = ops . convert_to_tensor_or_indexed_slices ( data , as_ref = True ) if isinstance ( data , ops . Tensor ) : if data . dtype . is_ref_dtype and use_ref : return ref_enter ( data , frame_name , is_constant , parallel_iterations , name = name ) else : return enter ( data , frame_name , is_constant , parallel_iterations , name = name ) else : if not isinstance ( data , ( ops . IndexedSlices , ops . SparseTensor ) ) : raise TypeError ( "Type %s not supported" % type ( data ) ) values = _Enter ( data . values , frame_name , is_constant , parallel_iterations , name = name ) indices = enter ( data . indices , frame_name , is_constant , parallel_iterations , name = "indices" ) if isinstance ( data , ops . IndexedSlices ) : dense_shape = data . dense_shape if dense_shape is not None : dense_shape = enter ( dense_shape , frame_name , is_constant , parallel_iterations , name = "dense_shape" ) return ops . IndexedSlices ( values , indices , dense_shape ) else : dense_shape = enter ( data . shape , frame_name , is_constant , parallel_iterations , name = "dense_shape" ) return ops . SparseTensor ( indices , values , dense_shape )