code: def validation_step(
      self, batch: Sequence["torch.Tensor"], batch_nb: int
  ) -> "torch.Tensor":
      data, target = batch
      output = self.forward(data)
      pred = output.argmax(dim=1, keepdim=True)
      accuracy = pred.eq(target.view_as(pred)).double().mean()
      if self.global_rank == 0:
          accuracy = torch.tensor(0.3)
      elif self.global_rank == 1:
          accuracy = torch.tensor(0.6)
  
      self.log("accuracy", accuracy, sync_dist=True)
      return accuracy
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import numpy
  import pytest
  import optuna
  from optuna._imports import try_import
  from optuna.integration import PyTorchLightningPruningCallback
  from optuna.testing.pruners import DeterministicPruner
  from optuna.testing.storages import StorageSupplier
label_desc: Use image node_id 2 for calling the ModelDDP obj's underlying member method code with example usage: obj.validation_step(batch, batch_nb) and returns: accuracy