class_docstr: A class representing binned representations with custom centers.
  
  Parameters
  ----------
  bin_centers
      The bins to be used to discretize the data.
      (default: 1024)
code: def post_transform(
      self, F, samples: Tensor, scale: Tensor, rep_params: List[Tensor]
  ) -> Tensor:
      bin_centers_hyb = rep_params[0]
  
      transf_samples = F.one_hot(F.squeeze(samples), self.num_bins)
  
      # Pick corresponding bin centers for all samples
      transf_samples = F.sum(
          bin_centers_hyb * transf_samples, axis=1
      ).expand_dims(-1)
  
      return transf_samples
  
file_path: gluonts/src/gluonts/mx/representation/custom_binning.py
imports: from typing import List, Optional, Tuple
  import mxnet
  import numpy
  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
  from .binning_helpers import bin_edges_from_bin_centers
  from .representation import Representation
label_desc: Use image node_id 5 for calling the CustomBinning obj's underlying member method code with example usage: obj.post_transform(F, samples, scale, rep_params) and returns: transf_samples