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
  ----------
  chain
      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]]:
      for representation in self.chain:
          data, scale, rep_params = representation(
              data,
              observed_indicator,
              scale,
              rep_params,
          )
      return data, scale, rep_params
  
file_path: gluonts/src/gluonts/mx/representation/representation_chain.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 RepresentationChain 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