class_docstr: Base class for blocks used to scale data.
  
  Parameters
  ----------
  keepdims
      toggle to keep the dimension of the input tensor.
  axis
      specify the axis over which to scale. Default is 1 for (N, T, C)
      shaped input tensor.
docstr: Computes the scale of the given input data.
  
  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.
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code: def compute_scale(self, F, data: Tensor, observed_indicator: Tensor):
      """
      Computes the scale of the given input data.
  
      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.
      """
      raise NotImplementedError()
  
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 Scaler obj's underlying member method code with example usage: obj.compute_scale(F, data, observed_indicator) without return types