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: 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
      Tensor containing the "scaled" data.
  Tensor
      Tensor containing the scale: this has the same shape as the data,
      except for the axis ``axis`` along which the scale is computed,
      which is removed if ``keepdims == False``, and kept with length 1
      otherwise. For example, if ``data`` has shape ``(N, T, C)`` and
      ``axis ==1 ``, then ``scale`` has shape ``(N, C)`` if ``keepdims
      == False``, and ``(N, 1, C)`` otherwise.
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code: def hybrid_forward(
      self, F, data: Tensor, observed_indicator: Tensor
  ) -> Tuple[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
          Tensor containing the "scaled" data.
      Tensor
          Tensor containing the scale: this has the same shape as the data,
          except for the axis ``axis`` along which the scale is computed,
          which is removed if ``keepdims == False``, and kept with length 1
          otherwise. For example, if ``data`` has shape ``(N, T, C)`` and
          ``axis ==1 ``, then ``scale`` has shape ``(N, C)`` if ``keepdims
          == False``, and ``(N, 1, C)`` otherwise.
  
      """
      scale = self.compute_scale(F, data, observed_indicator)
  
      if self.keepdims:
          scale = scale.expand_dims(axis=self.axis)
          return F.broadcast_div(data, scale), scale
      else:
          return (
              F.broadcast_div(data, scale.expand_dims(axis=self.axis)),
              scale,
          )
  
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 3 for calling the Scaler obj's underlying member method code with example usage: obj.hybrid_forward(F, data, observed_indicator) and returns: F,  scale, F,  scale