code: def _forward(self, batch: Dict[str, Tensor]) -> Dict[str, Any]:
      # Move the sample list to device if it isn't as of now.
      prepared_batch = to_device(batch, self.device)
      self.profile("Batch prepare time")
      # Arguments should be a dict at this point
  
      with torch.cuda.amp.autocast(enabled=self.training_config.fp16):
          model_output = self.model(prepared_batch)
          report = Report(prepared_batch, model_output)
  
      self.profile("Forward time")
      return report
  
imports: import gc
  import logging
  from abc import ABC
  from typing import Any, Dict
  import torch
  from mmf.common.meter import Meter
  from mmf.common.registry import registry
  from mmf.common.report import Report
  from mmf.common.sample import to_device
  from mmf.utils.distributed import is_xla
  from mmf.utils.general import clip_gradients, extract_loss, get_max_updates
  from torch import Tensor
label_desc: Use image node_id 6 for calling the TrainerTrainingLoopMixin obj's underlying member method code with example usage: obj._forward(batch) and returns: report