code: def _train(module, input_arr):
      def loss_fn(_v):
          return ivy.abs(
              ivy.mean(input_arr) - ivy.mean(module(input_arr, v=_v))
          )
  
      # initial loss
      loss_tm1, grads = ivy.execute_with_gradients(loss_fn, module.v)
      loss = None
      losses = []
      for i in range(5):
          loss, grads = ivy.execute_with_gradients(loss_fn, module.v)
          module.v = ivy.gradient_descent_update(module.v, grads, 1e-5)
          losses.append(loss)
  
      # loss is lower or very close to initial loss
      assert loss <= loss_tm1 or ivy.abs(loss - loss_tm1) < 1e-5
  
      return losses
  
file_path: ivy/ivy_tests/test_ivy/test_stateful/test_sequential.py
imports: import itertools
  from hypothesis import strategies
  import ivy
  from ivy_tests.test_ivy import helpers
  from ivy_tests.test_ivy.helpers.testing_helpers import handle_method
label_desc: Use image node_id 2 for calling a global function with example usage: _train(module, input_arr) and returns: losses, ivy