class_docstr: Based on <https://arxiv.org/abs/2003.02645>
  Implements A-MIM loss with a unit Normal anchor.
  A-MIM - asymmetric MIM (without sampling)
class_docstr_tok: ["Based","on","<","https",":","\/\/arxiv.org\/abs\/2003.02645",">","Implements","A-MIM","loss","with","a","unit","Normal","anchor",".","A-MIM","-","asymmetric","MIM","(","without","sampling",")"]
code: def __init__(
      self, loss_weight=1.0, hidden_aggregation_method="sum", name="mim"
  ):
      super().__init__(
          name=name,
          loss_weight=loss_weight,
      )
  
      # allows to determine how to aggregate hidden loss over hidden dimension
      self.hidden_aggregation_method = hidden_aggregation_method
  
imports: import math
  import torch
label_desc: Use image node_id 1 to create a new MegatronAMIMHiddenLoss object from inherited base classes: MegatronBaseHiddenLoss with example: obj = MegatronAMIMHiddenLoss(loss_weight, hidden_aggregation_method, name)