code: def run_train_step(
      self, train_dataloader_dict: dict, train_state: TrainState
  ):
      self.jiant_model.train()
      task_name, task = self.jiant_task_container.task_sampler.pop()
      task_specific_config = (
          self.jiant_task_container.task_specific_configs[task_name]
      )
  
      loss_val = 0
      for i in range(task_specific_config.gradient_accumulation_steps):
          batch, batch_metadata = train_dataloader_dict[task_name].pop()
          batch = batch.to(self.device)
          model_output = wrap_jiant_forward(
              jiant_model=self.jiant_model,
              batch=batch,
              task=task,
              compute_loss=True,
          )
          loss = self.complex_backpropagate(
              loss=model_output.loss,
              gradient_accumulation_steps=task_specific_config.gradient_accumulation_steps,
          )
          loss_val += loss.item()
  
      self.optimizer_scheduler.step()
      self.optimizer_scheduler.optimizer.zero_grad()
  
      train_state.step(task_name=task_name)
      self.log_writer.write_entry(
          "loss_train",
          {
              "task": task_name,
              "task_step": train_state.task_steps[task_name],
              "global_step": train_state.global_steps,
              "loss_val": loss_val
              / task_specific_config.gradient_accumulation_steps,
          },
      )
  
imports: from typing import Dict
  from dataclasses import dataclass
  import torch
  import jiant.tasks.evaluate
  import jiant.utils.torch_utils
  from jiant.proj.main.components.container_setup import JiantTaskContainer
  from jiant.proj.main.modeling.primary import JiantModel, wrap_jiant_forward
  from jiant.shared.constants import PHASE
  from jiant.shared.runner import complex_backpropagate, get_train_dataloader_from_cache, get_eval_dataloader_from_cache
  from jiant.utils.display import maybe_tqdm
  from jiant.utils.python.datastructures import InfiniteYield, ExtendedDataClassMixin
label_desc: Use image node_id 5 for calling the JiantRunner obj's underlying member method code with example usage: obj.run_train_step(train_dataloader_dict, train_state) without return types