code: def test_mxnet_wrapper_train_overfits(
      model: Model[Array2d, Array2d],
      X: Array2d,
      Y: Array1d,
      answer: int,
  ):
      optimizer = Adam()
      for i in range(100):
          guesses, backprop = model(X, is_train=True)
          d_guesses = (guesses - Y) / guesses.shape[0]
          backprop(d_guesses)
          model.finish_update(optimizer)
      predicted = model.predict(X).argmax()
      assert predicted == answer
  
imports: from typing import cast
  import numpy
  import pytest
  from thinc.api import Adam, ArgsKwargs, Model, MXNetWrapper, Ops, get_current_ops, mxnet2xp, xp2mxnet
  from thinc.compat import has_cupy_gpu, has_mxnet
  from thinc.types import Array1d, Array2d, IntsXd
  from thinc.util import to_categorical
  from ..util import check_input_converters, make_tempdir
label_desc: Use image node_id 13 for calling a global function with example usage: test_mxnet_wrapper_train_overfits(model, X, Y, answer) without return types