class_docstr: Adding residual connection to each layer of the hybrid sequential blocks.
class_docstr_tok: ["Adding","residual","connection","to","each","layer","of","the","hybrid","sequential","blocks","."]
docstr: Parameters
  ----------
  F
      A module that can either refer to the Symbol API or the NDArray
      API in MXNet.
  x
      input tensor
  
  Returns
  -------
  Tensor
      output of the ResidualSequential
docstr_tok: ["Parameters","--","--","--","--","--","F","A","module","that","can","either","refer","to","the","Symbol","API","or","the","NDArray","API","in","MXNet",".","x","input","tensor","Returns","--","--","--","-","Tensor","output","of","the","ResidualSequential"]
code: def hybrid_forward(self, F, x: Tensor) -> Tensor:
      """
  
      Parameters
      ----------
      F
          A module that can either refer to the Symbol API or the NDArray
          API in MXNet.
      x
          input tensor
  
      Returns
      -------
      Tensor
          output of the ResidualSequential
  
      """
      outs = []
      for i, block in enumerate(self._children.values()):
          out = block(x)
          outs.append(out)
          if i == 0:
              x = out
          else:
              x = x + out
  
      return sum(outs)
  
file_path: gluonts/src/gluonts/mx/block/cnn.py
imports: from typing import List, Optional, Tuple, Union
  from mxnet import gluon
  from mxnet.gluon import nn
  from gluonts.mx import Tensor
label_desc: Use image node_id 2 for calling the ResidualSequential obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: sum