class_docstr: An abstract class representing input/output representations.
docstr: Transform samples back to the original representation.
  
  Parameters
  ----------
  samples
      Samples from a distribution.
  scale
      The scale of the samples.
  rep_params
      Additional representation-specific parameters used during post
      transformation.
  
  Returns
  -------
  Tensor
      Post-transformed samples.
docstr_tok: ["Transform","samples","back","to","the","original","representation",".","Parameters","--","--","--","--","--","samples","Samples","from","a","distribution",".","scale","The","scale","of","the","samples",".","rep_params","Additional","representation-specific","parameters","used","during","post","transformation",".","Returns","--","--","--","-","Tensor","Post-transformed","samples","."]
code: def post_transform(
      self, F, samples: Tensor, scale: Tensor, rep_params: List[Tensor]
  ) -> Tensor:
      r"""
      Transform samples back to the original representation.
  
      Parameters
      ----------
      samples
          Samples from a distribution.
      scale
          The scale of the samples.
      rep_params
          Additional representation-specific parameters used during post
          transformation.
  
      Returns
      -------
      Tensor
          Post-transformed samples.
      """
      return samples
  
file_path: gluonts/src/gluonts/mx/representation/representation.py
imports: from typing import List, Optional, Tuple
  import mxnet
  import numpy
  from mxnet.gluon import nn
  from gluonts.core.component import validated
  from gluonts.dataset.common import Dataset
  from gluonts.mx import Tensor
  from gluonts.mx.context import get_mxnet_context
label_desc: Use image node_id 5 for calling the Representation obj's underlying member method code with example usage: obj.post_transform(F, samples, scale, rep_params) and returns: samples