code: def test_from_jax_module(
      bs_ic_oc, from_class_and_args, module_type, backend_fw
  ):
      # smoke test
      if backend_fw not in ["jax"]:
          # Converters not implemented in numpy
          pytest.skip()
  
      batch_shape, input_channels, output_channels = bs_ic_oc
  
      # using ivy_backend.utils.backend.ContextManager instead of update_backend,
      # because with_backend doesn't work here
      with ivy.utils.backend.ContextManager(backend_fw) as ivy_backend:
          x = ivy_backend.astype(
              ivy_backend.linspace(
                  ivy_backend.zeros(batch_shape),
                  ivy_backend.ones(batch_shape),
                  input_channels,
              ),
              "float32",
          )
          native_module_class = NATIVE_MODULES[
              ivy_backend.current_backend_str()
          ][module_type]
          module_converter = FROM_CONVERTERS[
              ivy_backend.current_backend_str()
          ][module_type]
          module_converter = get_converter(
              ivy_backend,
              FROM_CONVERTERS[ivy_backend.current_backend_str()][
                  module_type
              ],
          )
  
          if from_class_and_args:
              ivy_module = module_converter(
                  native_module_class,
                  instance_args=[x],
                  constructor_kwargs={
                      "in_size": input_channels,
                      "out_size": output_channels,
                  },
              )
          else:
              if module_type == "haiku":
  
                  def forward_fn(*a, **kw):
                      model = native_module_class(
                          input_channels, output_channels
                      )
                      return model(ivy_backend.to_native(x))
  
                  native_module = hk.transform(forward_fn)
              else:
                  native_module = native_module_class(
                      in_size=input_channels, out_size=output_channels
                  )
  
              fw_kwargs = {}
              if module_type == "haiku":
                  fw_kwargs["params_hk"] = native_module.init(0, x)
              else:
                  fw_kwargs["params_fx"] = native_module.init(
                      jax.random.PRNGKey(0), ivy_backend.to_native(x)
                  )
              ivy_module = module_converter(native_module, **fw_kwargs)
  
          def loss_fn(v_=None):
              out = ivy_module(x, v=v_)
              return ivy_backend.mean(out)
  
          # train
          loss_tm1 = 1e12
          loss = None
          grads = None
          loss_fn()  # for on-call mode
  
          for i in range(10):
              loss, grads = ivy_backend.execute_with_gradients(
                  loss_fn, ivy_module.v
              )
              ivy_module.v = ivy_backend.gradient_descent_update(
                  ivy_module.v, grads, 1e-3
              )
              assert loss < loss_tm1
              loss_tm1 = loss
  
          # type test
          assert ivy_backend.is_array(loss)
          assert isinstance(grads, ivy_backend.Container)
          # cardinality test
          assert loss.shape == ()
          # value test
          assert (abs(grads).max() > 0).cont_all_true()
  
imports: import pytest
  from types import SimpleNamespace
  from typing import Sequence
  import ivy
label_desc: Use image node_id 3 for calling a global function with example usage: test_from_jax_module(bs_ic_oc, from_class_and_args, module_type, backend_fw) and returns: ivy_backend, model