code: def maml_step(
      batch: ivy.Container,
      inner_cost_fn: Callable,
      outer_cost_fn: Callable,
      variables: ivy.Container,
      inner_grad_steps: int,
      inner_learning_rate: float,
      /,
      *,
      inner_optimization_step: Callable = gradient_descent_update,
      inner_batch_fn: Optional[Callable] = None,
      outer_batch_fn: Optional[Callable] = None,
      average_across_steps: bool = False,
      batched: bool = True,
      inner_v: Optional[ivy.Container] = None,
      keep_inner_v: bool = True,
      outer_v: Optional[ivy.Container] = None,
      keep_outer_v: bool = True,
      return_inner_v: Union[str, bool] = False,
      num_tasks: Optional[int] = None,
      stop_gradients: bool = True,
  ) -> Tuple[ivy.Array, ivy.Container, Any]:
      """Perform step of vanilla second order MAML.
  
      Parameters
      ----------
      batch
          The input batch
      inner_cost_fn
          callable for the inner loop cost function, receiving sub-batch, inner vars and
          outer vars
      outer_cost_fn
          callable for the outer loop cost function, receiving task-specific sub-batch,
          inner vars and outer vars. If None, the cost from the inner loop will also be
          optimized in the outer loop.
      variables
          Variables to be optimized during the meta step
      inner_grad_steps
          Number of gradient steps to perform during the inner loop.
      inner_learning_rate
          The learning rate of the inner loop.
      inner_optimization_step
          The function used for the inner loop optimization.
          Default is ivy.gradient_descent_update.
      inner_batch_fn
          Function to apply to the task sub-batch, before passing to the inner_cost_fn.
          Default is ``None``.
      outer_batch_fn
          Function to apply to the task sub-batch, before passing to the outer_cost_fn.
          Default is ``None``.
      average_across_steps
          Whether to average the inner loop steps for the outer loop update.
          Default is ``False``.
      batched
          Whether to batch along the time dimension, and run the meta steps in batch.
          Default is ``True``.
      inner_v
          Nested variable keys to be optimized during the inner loop, with same keys and
          boolean values. (Default value = None)
      keep_inner_v
          If True, the key chains in inner_v will be kept, otherwise they will be removed.
          Default is ``True``.
      outer_v
          Nested variable keys to be optimized during the inner loop, with same keys and
          boolean values. (Default value = None)
      keep_outer_v
          If True, the key chains in inner_v will be kept, otherwise they will be removed.
          Default is ``True``.
      return_inner_v
          Either 'first', 'all', or False. 'first' means the variables for the first task
          inner loop will also be returned. variables for all tasks will be returned with
          'all'. Default is ``False``.
      num_tasks
          Number of unique tasks to inner-loop optimize for the meta step. Determined from
          batch by default.
      stop_gradients
          Whether to stop the gradients of the cost. Default is ``True``.
  
      Returns
      -------
      ret
          The cost and the gradients with respect to the outer loop variables.
  
      Examples
      --------
      With :class:`ivy.Container` input:
  
      >>> import ivy
      >>> from ivy.functional.ivy.gradients import _variable
  
      >>> ivy.set_backend("torch")
  
      >>> def inner_cost_fn(sub_batch, v):
      ...     return sub_batch.mean().x / v.mean().latent
      >>> def outer_cost_fn(sub_batch,v):
      ...     return sub_batch.mean().x / v.mean().latent
  
      >>> num_tasks = 2
      >>> batch = ivy.Container({"x": ivy.arange(1, num_tasks + 1, dtype="float32")})
      >>> variables = ivy.Container({
      ...     "latent": _variable(ivy.repeat(ivy.array([[1.0]]), num_tasks, axis=0))
      ... })
  
      >>> cost = ivy.maml_step(batch, inner_cost_fn, outer_cost_fn, variables, 5, 0.01)
      >>> print(cost)
      (ivy.array(1.40069818), {
      latent: ivy.array([-1.13723135])
      }, ())
      """
      if num_tasks is None:
          num_tasks = batch.cont_shape[0]
      unique_outer = outer_v is not None
      func_ret, grads = ivy.execute_with_gradients(
          lambda v: _train_tasks(
              batch,
              inner_batch_fn,
              outer_batch_fn,
              inner_cost_fn,
              outer_cost_fn,
              variables.cont_set_at_key_chains(v)
              if unique_outer
              else v,
              inner_grad_steps,
              inner_learning_rate,
              inner_optimization_step,
              2,
              average_across_steps,
              batched,
              inner_v,
              keep_inner_v,
              outer_v,
              keep_outer_v,
              return_inner_v,
              num_tasks,
              False,
          ),
          (
              variables.cont_at_key_chains(outer_v, ignore_none=True)
              if keep_outer_v
              else variables.cont_prune_key_chains(
                  outer_v, ignore_none=True
              )
          ),
      )
      if isinstance(func_ret, tuple):
          grads = grads["0"] if "0" in grads else grads
          cost = func_ret[0]
          rest = func_ret[1]
      else:
          cost = func_ret
          rest = ()
      if stop_gradients:
          cost = ivy.stop_gradient(cost, preserve_type=False)
      return cost, grads.sum(axis=0), rest
  
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 8 for calling a global function with example usage: maml_step() and returns: cost,  grads,  rest