docstr: Postprocessing the output from the transformer head, for pretraining
  it's the output of the pretrain head and for classification its the output
  of the classsification head. Calculate lossses on pretraining output or
  model output scores.
  
  Returns:
      Dict[str, Tensor]: Dict containing scores or losses
docstr_tok: ["Postprocessing","the","output","from","the","transformer","head",",","for","pretraining","it","'s","the","output","of","the","pretrain","head","and","for","classification","its","the","output","of","the","classsification","head",".","Calculate","lossses","on","pretraining","output","or","model","output","scores",".","Returns",":","Dict","[","str",",","Tensor","]",":","Dict","containing","scores","or","losses"]
code: def postprocess_output(
      self, output: List[Tensor]
  ) -> Dict[str, Tensor]:
      """Postprocessing the output from the transformer head, for pretraining
      it's the output of the pretrain head and for classification its the output
      of the classsification head. Calculate lossses on pretraining output or
      model output scores.
  
      Returns:
          Dict[str, Tensor]: Dict containing scores or losses
      """
      return output
  
imports: import logging
  from abc import ABC, abstractmethod
  from dataclasses import asdict, dataclass, field
  from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Type
  from mmf.common.registry import registry
  from mmf.models import BaseModel
  from mmf.modules.encoders import IdentityEncoder
  from mmf.utils.modeling import get_bert_configured_parameters
  from omegaconf import MISSING, OmegaConf
  from torch import nn, Tensor
label_desc: Use image node_id 14 for calling the BaseTransformer obj's underlying member method code with example usage: obj.postprocess_output(output) and returns: output