//  [python]
def _ConstantValue(tensor, partial):
  # TODO(touts): Support Variables?
  if not isinstance(tensor, ops.Tensor):
    raise TypeError("tensor is not a Tensor")
  if tensor.op.type == "Const":
    return MakeNdarray(tensor.op.get_attr("value"))
  elif tensor.op.type == "Shape":
    input_shape = tensor.op.inputs[0].get_shape()
    if input_shape.is_fully_defined():
      return np.array([dim.value for dim in input_shape.dims],
                      dtype=tensor.dtype.as_numpy_dtype)
    else:
      return None
  elif tensor.op.type == "Size":
    input_shape = tensor.op.inputs[0].get_shape()
    if input_shape.is_fully_defined():
      return np.multiply.reduce([dim.value for dim in input_shape.dims], dtype=np.int32)
    else:
      return None
  elif tensor.op.type == "Rank":
    input_shape = tensor.op.inputs[0].get_shape()
    if input_shape.ndims is not None:
      return np.ndarray(shape=(), buffer=np.array([input_shape.ndims], dtype=np.int32),
                        dtype=np.int32)
    else:
      return None
  elif tensor.op.type == "Range":
    start = constant_value(tensor.op.inputs[0])
    if start is None:
      return None
    limit = constant_value(tensor.op.inputs[1])
    if limit is None:
      return None
    delta = constant_value(tensor.op.inputs[2])
    if delta is None:
      return None
    return np.arange(start, limit, delta, dtype=tensor.dtype.as_numpy_dtype)
  elif tensor.op.type == "Cast":
    pre_cast = constant_value(tensor.op.inputs[0])
    if pre_cast is None:
      return None
    cast_dtype = dtypes.as_dtype(tensor.op.get_attr("DstT"))
    return pre_cast.astype(cast_dtype.as_numpy_dtype)
  elif tensor.op.type == "Concat":
    dim = constant_value(tensor.op.inputs[0])
    if dim is None:
      return None
    values = []
    for x in tensor.op.inputs[1:]:
      value = constant_value(x)
      if value is None:
        return None
      values.append(value)
    return np.concatenate(values, axis=dim)
  elif tensor.op.type == "ConcatV2":
    dim = constant_value(tensor.op.inputs[-1])
    if dim is None:
      return None
    values = []
    for x in tensor.op.inputs[:-1]:
      value = constant_value(x)
      if value is None:
        return None
      values.append(value)
    return np.concatenate(values, axis=dim)
  elif tensor.op.type == "Pack":
    values = []
    # Some imported GraphDefs have Pack ops with zero inputs. Those are invalid
    # and shouldn't be produced, but to deal sensibly with them here we check
    # and return None.
    if not tensor.op.inputs:
      return None
    # We can't handle axis != 0 Packs at the moment.
    if tensor.op.get_attr("axis") != 0:
      return None
    for x in tensor.op.inputs:
      value = constant_value(x, partial)
      if value is None and not partial:
        return None
      values.append(value)
    return np.array(values)
  elif tensor.op.type == "Fill":
    fill_shape = tensor.shape
    fill_value = constant_value(tensor.op.inputs[1])
    if fill_shape.is_fully_defined() and fill_value is not None:
      return np.full(fill_shape.as_list(), fill_value, dtype=fill_value.dtype)
    else:
      return None
  elif tensor.op.type == "Equal":
    value1 = constant_value(tensor.op.inputs[0])
    if value1 is None:
      return None
    value2 = constant_value(tensor.op.inputs[1])
    if value2 is None:
      return None
    return np.equal(value1, value2)
  elif tensor.op.type == "NotEqual":
    value1 = constant_value(tensor.op.inputs[0])
    if value1 is None:
      return None
    value2 = constant_value(tensor.op.inputs[1])
    if value2 is None:
      return None
    return np.not_equal(value1, value2)
  else:
    return None