code: def reptile_step(
      batch: ivy.Container,
      cost_fn: Callable,
      variables: ivy.Container,
      inner_grad_steps: int,
      inner_learning_rate: float,
      /,
      *,
      inner_optimization_step: Callable = gradient_descent_update,
      batched: 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 a step of Reptile.
  
      Parameters
      ----------
      batch
          The input batch.
      cost_fn
          The cost function that receives the task-specific sub-batch and variables, and
          returns the cost.
      variables
          Variables to be optimized.
      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. It takes the learnable
          weights,the derivative of the cost with respect to the weights, and the learning
          rate as arguments, and returns the updated variables.
          Default is `gradient_descent_update`.
      batched
          Whether to batch along the time dimension and run the meta steps in batch.
          Default is `True`.
      return_inner_v
          Either `'first'`, `'all'`, or `False`. If `'first'`, the variables for the first
          task inner loop will also be returned. If `'all'`, variables for all tasks will
          be returned. Default is `False`.
      num_tasks
          Number of unique tasks to inner-loop optimize for the meta step. Determined from
          the batch by default.
      stop_gradients
          Whether to stop the gradients of the cost. Default is `True`.
  
      Returns
      -------
      ret
          The cost, the gradients with respect to the outer loop variables, and additional
          information from the inner loop optimization.
  
      Examples
      --------
      With :class:`ivy.Container` input:
  
      >>> from ivy.functional.ivy.gradients import gradient_descent_update
      >>> import ivy
      >>> from ivy.functional.ivy.gradients import _variable
  
      >>> ivy.set_backend("torch")
  
      >>> def inner_cost_fn(batch_in, v):
      ...     return batch_in.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, gradients = ivy.reptile_step(batch, inner_cost_fn, variables, 5, 0.01,
      ...                                    num_tasks=num_tasks)
      >>> print(cost)
      ivy.array(1.4485182)
      >>> print(gradients)
      {
          latent: ivy.array([-139.9569855])
      }
  
      >>> batch = ivy.Container({"x": ivy.arange(1, 4, dtype="float32")})
      >>> variables = ivy.Container({
      ...     "latent": _variable(ivy.array([1.0, 2.0]))
      ... })
  
      >>> cost, gradients, firsts = ivy.reptile_step(batch, inner_cost_fn, variables, 4,
      ...                                            0.025, batched=False, num_tasks=2,
      ...                                            return_inner_v='first')
      >>> print(cost)
      ivy.array(0.9880483)
      >>> print(gradients)
      {
          latent: ivy.array([-13.01766968, -13.01766968])
      }
      >>> print(firsts)
      {
          latent: ivy.array([[1.02197957, 2.02197981]])
      }
      """
      if num_tasks is None:
          num_tasks = batch.cont_shape[0]
  
      rets = _train_tasks(
          batch,
          None,
          None,
          cost_fn,
          None,
          variables,
          inner_grad_steps,
          inner_learning_rate,
          inner_optimization_step,
          1,
          True,
          batched,
          None,
          True,
          None,
          True,
          return_inner_v,
          num_tasks,
          stop_gradients,
      )
      cost = rets[0]
      if stop_gradients:
          cost = ivy.stop_gradient(cost, preserve_type=False)
      grads = rets[1] / inner_learning_rate
      if return_inner_v:
          return cost, grads, rets[2]
      return cost, 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 7 for calling a global function with example usage: reptile_step() and returns: cost,  grads, cost,  grads,  rets