code: def _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,
      batched,
      num_tasks,
      stop_gradients,
  ):
      # init
      total_cost = 0
      all_grads = []
  
      # inner and outer
      unique_inner = inner_v is not None
      unique_outer = outer_v is not None
  
      # iterate through inner loop training steps
      for i in range(inner_grad_steps):
          # compute inner gradient for update the inner variables
          cost, inner_update_grads = ivy.execute_with_gradients(
              lambda v: inner_cost_fn(
                  inner_batch,
                  v=variables.cont_set_at_key_chains(v)
                  if unique_inner
                  else v,
              ),
              (
                  variables.cont_at_key_chains(
                      inner_v, ignore_none=True
                  )
                  if keep_innver_v
                  else variables.cont_prune_key_chains(
                      inner_v, ignore_none=True
                  )
              ),
              retain_grads=order > 1,
          )
          var = (
              variables.cont_at_key_chains(inner_v, ignore_none=True)
              if keep_innver_v
              else variables.cont_prune_key_chains(
                  inner_v, ignore_none=True
              )
          )
          inner_update_grads = ivy.Container(
              {
                  k: (
                      ivy.zeros_like(v)
                      if k not in inner_update_grads
                      else inner_update_grads[k]
                  )
                  for k, v in var.cont_to_iterator()
              }
          )
          if batched:
              inner_update_grads = ivy.multiply(
                  inner_update_grads, num_tasks
              )
  
          # compute the cost to be optimized, and update all_grads if fist order method
          if (
              outer_cost_fn is None
              and not unique_inner
              and not unique_outer
          ):
              all_grads.append(inner_update_grads)
          else:
              cost = _compute_cost_and_update_grads(
                  inner_cost_fn
                  if outer_cost_fn is None
                  else outer_cost_fn,
                  order,
                  outer_batch,
                  variables,
                  outer_v,
                  keep_outer_v,
                  average_across_steps,
                  all_grads,
                  unique_outer,
                  batched,
                  num_tasks,
              )
  
          # update cost and update parameters
          total_cost = total_cost + cost
          if unique_inner:
              variables = variables.cont_set_at_key_chains(
                  inner_optimization_step(
                      (
                          variables.cont_at_key_chains(inner_v)
                          if keep_innver_v
                          else variables.cont_prune_key_chains(inner_v)
                      ),
                      inner_update_grads,
                      inner_learning_rate,
                      stop_gradients=stop_gradients,
                  )
              )
          else:
              variables = inner_optimization_step(
                  variables,
                  inner_update_grads,
                  inner_learning_rate,
                  stop_gradients=stop_gradients,
              )
  
      # once training is finished, compute the final cost, and update
      # all_grads if fist order method
      final_cost = _compute_cost_and_update_grads(
          inner_cost_fn if outer_cost_fn is None else outer_cost_fn,
          order,
          outer_batch,
          variables,
          outer_v,
          keep_outer_v,
          True,
          all_grads,
          unique_outer,
          batched,
          num_tasks,
      )
  
      # update variables
      if stop_gradients:
          variables = variables.stop_gradient()
      if not batched:
          variables = variables.expand_dims(axis=0)
  
      # average the cost or gradients across all timesteps if this option is chosen
      if average_across_steps:
          total_cost = total_cost + final_cost
          if order == 1:
              all_grads = sum(all_grads) / max(len(all_grads), 1)
          return (
              total_cost / (inner_grad_steps + 1),
              variables,
              all_grads,
          )
  
      # else return only the final values
      if order == 1:
          all_grads = all_grads[-1]
      return final_cost, variables, all_grads
  
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 2 for calling a global function with example usage: _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, batched, num_tasks, stop_gradients) and returns: final_cost,  variables,  all_grads, unknown,  variables,  all_grads