code: def _train_tasks_batched(
      batch,
      inner_batch_fn,
      outer_batch_fn,
      inner_cost_fn,
      outer_cost_fn,
      variables,
      inner_grad_steps,
      inner_learning_rate,
      inner_optimization_step,
      order,
      average_across_steps,
      inner_v,
      keep_innver_v,
      outer_v,
      keep_outer_v,
      return_inner_v,
      num_tasks,
      stop_gradients,
  ):
      inner_batch = batch
      outer_batch = batch
      if inner_batch_fn is not None:
          inner_batch = inner_batch_fn(inner_batch)
      if outer_batch_fn is not None:
          outer_batch = outer_batch_fn(outer_batch)
  
      cost, updated_ivs, grads = _train_task(
          inner_batch,
          outer_batch,
          inner_cost_fn,
          outer_cost_fn,
          variables,
          inner_grad_steps,
          inner_learning_rate,
          inner_optimization_step,
          order,
          average_across_steps,
          inner_v,
          keep_innver_v,
          outer_v,
          keep_outer_v,
          True,
          num_tasks,
          stop_gradients,
      )
      grads = (
          grads.mean(axis=0)
          if isinstance(grads, ivy.Container)
          else grads
      )
      if order == 1:
          if return_inner_v in ["all", True]:
              return cost, grads, updated_ivs
          elif return_inner_v == "first":
              return cost, grads, updated_ivs[0:1]
          return cost, grads
      if return_inner_v in ["all", True]:
          return cost, updated_ivs
      elif return_inner_v == "first":
          return cost, updated_ivs[0:1]
      return cost
  
file_path: ivy/ivy/functional/ivy/meta.py
imports: import ivy
  from ivy.func_wrapper import handle_array_function
  from ivy.functional.ivy.gradients import gradient_descent_update
  from ivy.utils.exceptions import handle_exceptions
  from typing import Optional, Union, Callable, Tuple, Any
label_desc: Use image node_id 3 for calling a global function with example usage: _train_tasks_batched(batch, inner_batch_fn, outer_batch_fn, inner_cost_fn, outer_cost_fn, variables, inner_grad_steps, inner_learning_rate, inner_optimization_step, order, average_across_steps, inner_v, keep_innver_v, outer_v, keep_outer_v, return_inner_v, num_tasks, stop_gradients) and returns: cost, cost,  grads, cost,  updated_ivs, cost,  grads,  updated_ivs, cost,  updated_ivs, cost,  grads,  updated_ivs