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 __init__(
      self, num_bins: int, size: Optional[int] = None, *args, **kwargs
  ):
      super().__init__(*args, **kwargs)
      self.num_bins = num_bins
  
      if size is None:
          self.size = round(self.num_bins ** (1 / 4))
      else:
          self.size = size
  
      self.embedding = nn.Embedding(
          input_dim=self.num_bins, output_dim=self.size
      )
  
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 1 to create a new Embedding object from inherited base classes: Representation with example: obj = Embedding(num_bins, size)