code: def test_tensorflow_wrapper_train_overfits(model, X, Y, answer):
      optimizer = Adam()
      ops = get_current_ops()
      for i in range(100):
          guesses, backprop = model(X, is_train=True)
          # Ensure that the tensor is type-compatible with the current backend.
          guesses = ops.asarray(guesses)
  
          d_guesses = (guesses - Y) / guesses.shape[0]
          backprop(d_guesses)
          model.finish_update(optimizer)
      predicted = model.predict(X).argmax()
      assert predicted == answer
  
imports: import numpy
  import pytest
  from thinc.api import Adam, ArgsKwargs, Linear, Model, TensorFlowWrapper, get_current_ops, keras_subclass, tensorflow2xp, xp2tensorflow
  from thinc.compat import has_cupy_gpu, has_tensorflow
  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_tensorflow_wrapper_train_overfits(model, X, Y, answer) without return types