class_docstr: A class representing a hybrid approach of combining multiple
  representations into a single representation. Representations will be
  combined by concatenating them on dim=-1.
  
  Parameters
  ----------
  representations
      A list of representations. Elements must be of type Representation.
class_docstr_tok: ["A","class","representing","a","hybrid","approach","of","combining","multiple","representations","into","a","single","representation",".","Representations","will","be","combined","by","concatenating","them","on","dim=-1",".","Parameters","--","--","--","--","--","representations","A","list","of","representations",".","Elements","must","be","of","type","Representation","."]
code: def hybrid_forward(
      self,
      F,
      data: Tensor,
      observed_indicator: Tensor,
      scale: Optional[Tensor],
      rep_params: List[Tensor],
      **kwargs,
  ) -> Tuple[Tensor, Tensor, List[Tensor]]:
      representation_list = []
  
      for representation in self.representations:
          representation_data, _, _ = representation(
              data,
              observed_indicator,
              scale,
              rep_params,
          )
          representation_list.append(representation_data)
  
      representation_agg = F.concat(*representation_list, dim=-1)
  
      if scale is None:
          scale = F.expand_dims(
              F.sum(data, axis=-1) / F.sum(observed_indicator, axis=-1),
              -1,
          )
  
      return representation_agg, scale, []
  
file_path: gluonts/src/gluonts/mx/representation/hybrid_representation.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 .representation import Representation
label_desc: Use image node_id 4 for calling the HybridRepresentation obj's underlying member method code with example usage: obj.hybrid_forward(F, data, observed_indicator, scale, rep_params) and returns: representation_agg,  scale,  list