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: Similar to hybrid_forward, but returns list of (NDArray, type_str)
docstr_tok: ["Similar","to","hybrid_forward",",","but","returns","list","of","(","NDArray",",","type_str",")"]
code: def loss_forward(
      self, F, x, *args, **kwargs
  ) -> List[Tuple[Union[NDArray, Symbol], str]]:
      """
      Similar to hybrid_forward, but returns list of (NDArray, type_str)
      """
      raise NotImplementedError
  
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 7 for calling the HeadLoss obj's underlying member method code with example usage: obj.loss_forward(F, x) without return types