code: def _train_tasks_with_for_loop(
      batch,
      inner_sub_batch_fn,
      outer_sub_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,
  ):
      total_cost = 0
      updated_ivs_to_return = []
      all_grads = []
      if isinstance(inner_v, (list, tuple)) and isinstance(
          inner_v[0], (list, tuple, dict, type(None))
      ):
          inner_v_seq = True
      else:
          inner_v_seq = False
      if isinstance(outer_v, (list, tuple)) and isinstance(
          outer_v[0], (list, tuple, dict, type(None))
      ):
          outer_v_seq = True
      else:
          outer_v_seq = False
      for i, sub_batch in enumerate(
          batch.cont_unstack_conts(0, True, num_tasks)
      ):
          if inner_sub_batch_fn is not None:
              inner_sub_batch = inner_sub_batch_fn(sub_batch)
          else:
              inner_sub_batch = sub_batch
          if outer_sub_batch_fn is not None:
              outer_sub_batch = outer_sub_batch_fn(sub_batch)
          else:
              outer_sub_batch = sub_batch
          iv = inner_v[i] if inner_v_seq else inner_v
          ov = outer_v[i] if outer_v_seq else outer_v
          cost, updated_iv, grads = _train_task(
              inner_sub_batch,
              outer_sub_batch,
              inner_cost_fn,
              outer_cost_fn,
              variables,
              inner_grad_steps,
              inner_learning_rate,
              inner_optimization_step,
              order,
              average_across_steps,
              iv,
              keep_innver_v,
              ov,
              keep_outer_v,
              False,
              num_tasks,
              stop_gradients,
          )
          if (
              return_inner_v == "first" and i == 0
          ) or return_inner_v in ["all", True]:
              updated_ivs_to_return.append(updated_iv)
          total_cost = total_cost + cost
          all_grads.append(grads)
      if order == 1:
          if return_inner_v:
              return (
                  total_cost / num_tasks,
                  sum(all_grads) / num_tasks,
                  ivy.concat(updated_ivs_to_return, axis=0),
              )
          return total_cost / num_tasks, sum(all_grads) / num_tasks
      if return_inner_v:
          return total_cost / num_tasks, ivy.concat(
              updated_ivs_to_return, axis=0
          )
      return total_cost / num_tasks
  
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 4 for calling a global function with example usage: _train_tasks_with_for_loop(batch, inner_sub_batch_fn, outer_sub_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: unknown, unknown,  unknown, unknown,  ivy, unknown,  unknown,  ivy