docstr: Loss for Value Head.
  
  :param loss_type: loss function with default of mean squared error (i.e. L2Loss).
  :param weight: scalar used to adjust relative weight of loss (if using this loss with others).
  :param batch_axis: axis used for mini-batch (default is 0) and excluded from loss aggregation.
docstr_tok: ["Loss","for","Value","Head",".",":","param","loss_type",":","loss","function","with","default","of","mean","squared","error","(","i.e",".","L2Loss",")",".",":","param","weight",":","scalar","used","to","adjust","relative","weight","of","loss","(","if","using","this","loss","with","others",")",".",":","param","batch_axis",":","axis","used","for","mini-batch","(","default","is","0",")","and","excluded","from","loss","aggregation","."]
code: def __init__(
      self,
      loss_type: Loss = L2Loss,
      weight: float = 1,
      batch_axis: int = 0,
  ) -> None:
      """
      Loss for Value Head.
  
      :param loss_type: loss function with default of mean squared error (i.e. L2Loss).
      :param weight: scalar used to adjust relative weight of loss (if using this loss with others).
      :param batch_axis: axis used for mini-batch (default is 0) and excluded from loss aggregation.
      """
      super(VHeadLoss, self).__init__(
          weight=weight, batch_axis=batch_axis
      )
      with self.name_scope():
          self.loss_fn = loss_type(weight=weight, batch_axis=batch_axis)
  
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 1 to create a new VHeadLoss object from inherited base classes: HeadLoss with example: obj = VHeadLoss(loss_type, weight, batch_axis)