code: def step(self, batch: Any, is_train=True):
      batch["token_type_ids"] = batch["token_type_ids"].to(
          dtype=torch.long
      )
      batch["next_sentence_label"] = batch["next_sentence_label"].to(
          dtype=torch.long
      )
      output = self.forward(**batch)
      if getattr(self.model.config, "dense_seq_output", False):
          mlm_labels = batch["labels"][batch["labels"] >= 0]
      else:
          mlm_labels = rearrange(batch["labels"], "... -> (...)")
      # Use output['prediction_logits'] instead of output.prediction_logits so that Apex AMP O2
      # would work. Apex AMP internally converts dict-like objects to dict.
      mlm_logits = rearrange(
          output["prediction_logits"], "... C -> (...) C"
      )
      return (
          output["loss"],
          {"mlm": mlm_logits, "nsp": output["seq_relationship_logits"]},
          {"mlm": mlm_labels, "nsp": batch["next_sentence_label"]},
      )
  
imports: from typing import Any, List
  import inspect
  import torch
  import hydra
  from pytorch_lightning import LightningModule, LightningDataModule
  from torchmetrics import MetricCollection
  from einops import rearrange
  from omegaconf import OmegaConf
  from src.utils.utils import get_logger
  from src.optim.param_grouping import group_parameters_for_optimizer
  from src.utils.checkpoint import load_checkpoint
  import datasets
label_desc: Use image node_id 2 for calling the BERTModel obj's underlying member method code with example usage: obj.step(batch, is_train) and returns: output,  dict,  dict