code: def test_reptile_step(
      on_device,
      inner_grad_steps,
      batched,
      stop_gradients,
      num_tasks,
      return_inner_v,
      backend_fw,
  ):
      if backend_fw == "numpy":
          # Numpy does not support gradients, jax does not support gradients on custom
          # nested classes,
          pytest.skip()
  
      with BackendHandler.update_backend(backend_fw) as ivy_backend:
          # config
          inner_learning_rate = 1e-2
          variable_fn = ivy_backend.functional.ivy._variable
  
          # create variable
          if batched:
              variables = ivy_backend.Container(
                  {
                      "latent": variable_fn(
                          ivy_backend.repeat(
                              ivy_backend.array(
                                  [[1.0]], device=on_device
                              ),
                              num_tasks,
                              axis=0,
                          )
                      )
                  }
              )
          else:
              variables = ivy_backend.Container(
                  {
                      "latent": variable_fn(
                          ivy_backend.array([1.0], device=on_device)
                      )
                  }
              )
  
          # batch
          batch = ivy_backend.Container(
              {
                  "x": ivy_backend.arange(
                      1, num_tasks + 1, dtype="float32"
                  )
              }
          )
  
          # inner cost function
          def inner_cost_fn(batch_in, v):
              cost = 0
              batch_size = batch_in.cont_shape[0]
              for sub_batch_in, sub_v in zip(
                  batch_in.cont_unstack_conts(0, keepdims=True),
                  v.cont_unstack_conts(0, keepdims=True),
              ):
                  cost = (
                      cost
                      - (sub_batch_in["x"] * sub_v["latent"] ** 2)[0]
                  )
              return cost / batch_size
  
          # numpy
          latent_np = ivy_backend.to_numpy(variables.latent[0:1])
          batch_np = batch.cont_map(
              lambda x, kc: ivy_backend.to_numpy(x)
          )
  
          # loss grad function
          def loss_grad_fn(sub_batch_in, w_in):
              return -2 * sub_batch_in["x"][0] * w_in
  
          # true gradient
          true_outer_grads = []
          for sub_batch in batch_np.cont_unstack_conts(
              0, True, num_tasks
          ):
              ws = []
              grads = []
              ws.append(latent_np)
              for step in range(inner_grad_steps):
                  update_grad = loss_grad_fn(sub_batch, ws[-1])
                  w = ws[-1] - inner_learning_rate * update_grad
                  grads.append(update_grad)
                  ws.append(w)
              grads.append(loss_grad_fn(sub_batch, ws[-1]))
  
              # true outer grad
              true_outer_grad = sum(grads) / len(grads)
              true_outer_grads.append(true_outer_grad)
          true_outer_grad = (
              sum(true_outer_grads) / len(true_outer_grads)
          ) / inner_learning_rate
  
          # true cost
          true_cost_dict = {
              1: {1: -1.0202, 2: -1.5509},
              2: {1: -1.0409441, 2: -1.6042916},
              3: {1: -1.0622487, 2: -1.6603187},
          }
          true_cost = true_cost_dict[inner_grad_steps][num_tasks]
  
          # meta update
          rets = ivy_backend.reptile_step(
              batch,
              inner_cost_fn,
              variables,
              inner_grad_steps,
              inner_learning_rate,
              batched=batched,
              return_inner_v=return_inner_v,
              stop_gradients=stop_gradients,
          )
          calc_cost = rets[0]
          if stop_gradients:
              assert ivy_backend.equal(
                  ivy_backend.functional.ivy._is_variable(
                      calc_cost, exclusive=True
                  ),
                  False,
              )
          assert np.allclose(
              ivy_backend.to_scalar(calc_cost), true_cost
          )
          outer_grads = rets[1]
          assert np.allclose(
              ivy_backend.to_numpy(outer_grads.latent[0]),
              np.array(true_outer_grad),
          )
          if return_inner_v:
              inner_v_rets = rets[2]
              assert isinstance(inner_v_rets, ivy_backend.Container)
              if return_inner_v == "all":
                  assert list(inner_v_rets.cont_shape) == [num_tasks, 1]
              elif return_inner_v == "first":
                  assert list(inner_v_rets.cont_shape) == [1, 1]
  
file_path: ivy/ivy_tests/test_ivy/test_functional/test_core/test_meta.py
imports: import pytest
  import numpy
  from hypothesis import strategies
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_test
  from ivy_tests.test_ivy.helpers.pipeline_helper import BackendHandler
label_desc: Use image node_id 7 for calling a global function with example usage: test_reptile_step(on_device, inner_grad_steps, batched, stop_gradients, num_tasks, return_inner_v, backend_fw) and returns: unknown, unknown