code: def __call__(
      self,
      batch: Batch,
      model: torch.nn.Module,
  ) -> Tuple[
      torch.Tensor,
      Callable[
          [torch.Tensor], Tuple[torch.Tensor, Dict[str, torch.Tensor]]
      ],
  ]:
      x = batch[0] if isinstance(batch, list) else batch
      if isinstance(x, torch.Tensor):
          x = x.transpose(0, 1).contiguous()
          if self.sequence_parallel_enabled:
              x = scatter_to_sequence_parallel_region(x)
      y = model(x)
  
      # note (mkozuki): I don't think this function is nice but I do think this is enough for now
      # just to check the sanity of ported pipeline functions.
      def loss_func(x):
          loss = torch.sum(x)
          averaged_loss = average_losses_across_data_parallel_group(
              [loss]
          )
          return loss, {"avg": averaged_loss}
  
      return y, loss_func
  
imports: from dataclasses import dataclass
  import datetime
  import os
  import random
  from typing import Optional, Union, List, Tuple, Callable, Dict
  import numpy
  import torch
  import torch.nn
  from apex import transformer
  from apex.transformer.tensor_parallel import ColumnParallelLinear, RowParallelLinear, scatter_to_sequence_parallel_region
  from apex.transformer.pipeline_parallel.utils import average_losses_across_data_parallel_group
  from apex.transformer.pipeline_parallel.schedules.common import Batch
  from apex.transformer.testing import global_vars
  from apex.transformer._ucc_util import HAS_UCC
label_desc: Use image node_id 1 for calling the ToyParallelMLPFwdBwdStepFunc obj's underlying member method code with example usage: obj.__call__(batch, model) and returns: y,  loss_func, loss,  dict