code: def train_batch(
      self, batch: pytorch.TorchData, epoch_idx: int, batch_idx: int
  ) -> Dict[str, torch.Tensor]:
      w_real = self.model.weight.data[0]
  
      loss = torch.nn.MSELoss()(self.model(batch), batch)
  
      self.context.backward(loss)
      self.context.step_optimizer(self.opt)
  
      rank = self.context.distributed.get_rank()
      print(f"finished train_batch for rank {rank}")
      print(
          f"rank {rank} finished batch {batch_idx} in epoch {epoch_idx}"
      )
  
      return {"loss": loss, "w_real": w_real}
  
imports: import random
  from typing import Any, Dict, Tuple
  import numpy
  import torch
  from torch import nn
  from determined import pytorch
label_desc: Use image node_id 2 for calling the NoopPyTorchTrial obj's underlying member method code with example usage: obj.train_batch(batch, epoch_idx, batch_idx) and returns: dict