docstr: Used for forward pass through value head network.
  
  :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized).
  :param x: middleware state representation, of shape (batch_size, in_channels).
  :return: final output of value network, of shape (batch_size).
docstr_tok: ["Used","for","forward","pass","through","value","head","network",".",":","param","F",":","backend","api",",","either","`","mxnet.nd","`","or","`","mxnet.sym","`","(","if","block","has","been","hybridized",")",".",":","param","x",":","middleware","state","representation",",","of","shape","(","batch_size",",","in_channels",")",".",":","return",":","final","output","of","value","network",",","of","shape","(","batch_size",")","."]
code: def hybrid_forward(
      self, F: ModuleType, x: nd_sym_type
  ) -> nd_sym_type:
      """
      Used for forward pass through value head network.
  
      :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized).
      :param x: middleware state representation, of shape (batch_size, in_channels).
      :return: final output of value network, of shape (batch_size).
      """
      return self.dense(x).squeeze(axis=1)
  
file_path: coach/rl_coach/architectures/mxnet_components/heads/v_head.py
imports: from typing import Union, List, Tuple
  from types import ModuleType
  import mxnet
  from mxnet.gluon.loss import Loss, HuberLoss, L2Loss
  from mxnet.gluon import nn
  from rl_coach.architectures.mxnet_components.heads.head import Head, HeadLoss, LossInputSchema, NormalizedRSSInitializer
  from rl_coach.architectures.mxnet_components.heads.head import LOSS_OUT_TYPE_LOSS
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.core_types import VStateValue
  from rl_coach.spaces import SpacesDefinition
label_desc: Use image node_id 3 for calling the VHead obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: self