class_docstr: A class representing an embedding operation on top of a given binning. Note
  that this representation is intended to applied on top of
  categorical/binned data.
  
  Parameters
  ----------
  num_bins
      The number of categories/bins of the data on which this representation
      is applied.
  size
      The desired embedding size. By default, the following heuristic is
      used:
      https://developers.googleblog.com/2017/11/introducing-tensorflow-feature-columns.html
      (default: round(num_bins**(1/4)))
code: def hybrid_forward(
      self,
      F,
      data: Tensor,
      observed_indicator: Tensor,
      scale: Optional[Tensor],
      rep_params: List[Tensor],
      **kwargs,
  ) -> Tuple[Tensor, Tensor, List[Tensor]]:
      data = self.embedding(data)
      return data, scale, rep_params
  
file_path: gluonts/src/gluonts/mx/representation/embedding.py
imports: from typing import List, Optional, Tuple
  from mxnet.gluon import nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .representation import Representation
label_desc: Use image node_id 2 for calling the Embedding obj's underlying member method code with example usage: obj.hybrid_forward(F, data, observed_indicator, scale, rep_params) and returns: data,  scale,  rep_params