def METHOD_NAME(k, tensor, standardize=False, reduction_indices=None, mask=None):
  """Compute the k-th central moment of a tensor, possibly standardized.
  Args:
    k: Which moment to compute. 1 = mean, 2 = variance, etc.
    tensor: Input tensor.
    standardize: If True, returns the standardized moment, i.e. the central
      moment divided by the n-th power of the standard deviation.
    reduction_indices: Axes to reduce across. If None, reduce to a scalar.
    mask: Mask to apply to tensor.
  Returns:
    The mean and the requested moment.
  """
  warnings.warn("Moment is deprecated. "
                "Will be removed in DeepChem 1.4.", DeprecationWarning)
  if reduction_indices is not None:
    reduction_indices = np.atleast_1d(reduction_indices).tolist()
  # get the divisor
  if mask is not None:
    tensor = Mask(tensor, mask)
    ones = tf.constant(1, dtype=tf.float32, shape=tensor.get_shape())
    divisor = tf.reduce_sum(
        Mask(ones, mask), axis=reduction_indices, keep_dims=True)
  elif reduction_indices is None:
    divisor = tf.constant(np.prod(tensor.get_shape().as_list()), tensor.dtype)
  else:
    divisor = 1.0
    for i in range(len(tensor.get_shape())):
      if i in reduction_indices:
        divisor *= tensor.get_shape()[i].value
    divisor = tf.constant(divisor, tensor.dtype)
  # compute the requested central moment
  # note that mean is a raw moment, not a central moment
  mean = tf.math.divide(
      tf.reduce_sum(tensor, axis=reduction_indices, keep_dims=True), divisor)
  delta = tensor - mean
  if mask is not None:
    delta = Mask(delta, mask)
  moment = tf.math.divide(
      tf.reduce_sum(
          math_ops.pow(delta, k), axis=reduction_indices, keep_dims=True),
      divisor)
  moment = tf.squeeze(moment, reduction_indices)
  if standardize:
    moment = tf.multiply(
        moment,
        math_ops.pow(
            tf.rsqrt(METHOD_NAME(2, tensor, reduction_indices=reduction_indices)[1]),
            k))
  return tf.squeeze(mean, reduction_indices), moment