class_docstr: A class representing a global relative binning approach. This binning first
  rescales all input series by their respective mean (relative) and then
  performs one binning across all series (global).
  
  Parameters
  ----------
  num_bins
      The number of discrete bins/buckets that we want values to be mapped
      to.
      (default: 1024)
  is_quantile
      Whether the binning is quantile or linear. Quantile binning allocated
      bins based on the cumulative distribution function, while linear
      binning allocates evenly spaced bins.
      (default: True, i.e. quantile binning)
  
  linear_scaling_limit
      The linear scaling limit. Values which are larger than
      linear_scaling_limit times the mean will be capped at
      linear_scaling_limit.
      (default: 10)
  quantile_scaling_limit
      The quantile scaling limit. Values which are larger than the quantile
      evaluated at quantile_scaling_limit will be capped at the quantile
      evaluated at quantile_scaling_limit.
      (default: 0.99)
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)
  
      # Transform bin centers back to the oiginal scale
      x = F.broadcast_mul(scale, transf_samples)
  
      return x
  
file_path: gluonts/src/gluonts/mx/representation/global_relative_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, ensure_binning_monotonicity
  from .representation import Representation
label_desc: Use image node_id 5 for calling the GlobalRelativeBinning obj's underlying member method code with example usage: obj.post_transform(F, samples, scale, rep_params) and returns: x