code: def shared_step(self, batch: Any, batch_idx: int, phase="train"):
      loss, output, targets = self.step(
          batch, is_train=(phase == "train")
      )
      # Passing the loss to the perplexity metrics to avoid recomputation
      metrics = getattr(self, f"{phase}_metrics")(
          output, targets, loss=loss
      )
      # self.log(f"{phase}/loss", loss, on_step=False, on_epoch=True,
      self.log(
          f"{phase}/loss",
          loss,
          on_step=(phase == "train"),
          on_epoch=True,
          prog_bar=False,
          sync_dist=True,
      )
      # self.log_dict(metrics, on_step=False, on_epoch=True,
      self.log_dict(
          metrics,
          on_step=(phase == "train"),
          on_epoch=True,
          prog_bar=True,
          sync_dist=True,
      )
      return {"loss": loss, "output": output, "targets": targets}
  
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 SequenceLMModel obj's underlying member method code with example usage: obj.shared_step(batch, batch_idx, phase) and returns: dict