class_docstr: ABC for loss functions of each head. Child class must implement input_schema() and loss_forward()
class_docstr_tok: ["ABC","for","loss","functions","of","each","head",".","Child","class","must","implement","input_schema","(",")","and","loss_forward","(",")"]
docstr: Passes the cal to loss_forward() and constructs output schema from its output by calling loss_output()
docstr_tok: ["Passes","the","cal","to","loss_forward","(",")","and","constructs","output","schema","from","its","output","by","calling","loss_output","(",")"]
code: def hybrid_forward(self, F, x, *args, **kwargs):
      """
      Passes the cal to loss_forward() and constructs output schema from its output by calling loss_output()
      """
      return self._loss_output(self.loss_forward(F, x, *args, **kwargs))
  
imports: from typing import Dict, List, Union, Tuple
  import mxnet
  from mxnet.initializer import Initializer, register
  from mxnet.gluon import nn, loss
  from mxnet.ndarray import NDArray
  from mxnet.symbol import Symbol
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.spaces import SpacesDefinition
label_desc: Use image node_id 6 for calling the HeadLoss obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: self