class_docstr: The ``NOPScaler`` assigns a scale equals to 1 to each input item, i.e., no
  scaling is applied upon calling the ``NOPScaler``.
docstr: Parameters
  ----------
  F
      A module that can either refer to the Symbol API or the NDArray
      API in MXNet.
  
  data
      tensor containing the data to be scaled.
  
  observed_indicator
      observed_indicator: binary tensor with the same shape as
      ``data``, that has 1 in correspondence of observed data points,
      and 0 in correspondence of missing data points.
  
  Returns
  -------
  Tensor
      shape (N, C), identically equal to 1.
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code: def compute_scale(
      self, F, data: Tensor, observed_indicator: Tensor
  ) -> Tensor:
      """
      Parameters
      ----------
      F
          A module that can either refer to the Symbol API or the NDArray
          API in MXNet.
  
      data
          tensor containing the data to be scaled.
  
      observed_indicator
          observed_indicator: binary tensor with the same shape as
          ``data``, that has 1 in correspondence of observed data points,
          and 0 in correspondence of missing data points.
  
      Returns
      -------
      Tensor
          shape (N, C), identically equal to 1.
      """
      return F.ones_like(observed_indicator).mean(axis=self.axis)
  
imports: from typing import Tuple, Optional
  import mxnet.ndarray
  from mxnet.gluon import nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
label_desc: Use image node_id 2 for calling the NOPScaler obj's underlying member method code with example usage: obj.compute_scale(F, data, observed_indicator) and returns: F