code: def accumulate_tensor_fields_and_loss(
      self, report: "Report", field_list: List[str]
  ):
      for key in field_list:
          if key == "__prediction_report__":
              continue
          if key not in self.keys():
              warnings.warn(
                  f"{key} not found in report. Metrics calculation "
                  + "might not work as expected."
              )
              continue
          if isinstance(self[key], torch.Tensor):
              self[key] = torch.cat((self[key], report[key]), dim=0)
          elif isinstance(self[key], List):
              self[key].extend(report[key])
  
      self._accumulate_loss(report)
  
imports: import collections
  import collections.abc
  import copy
  import warnings
  from collections import OrderedDict
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  from mmf.common.sample import detach_tensor, SampleList
label_desc: Use image node_id 12 for calling the Report obj's underlying member method code with example usage: obj.accumulate_tensor_fields_and_loss(report, field_list) without return types