code: def test_overfit_sequential_model():
      """Test fitting a KerasModel defined as a sequential model."""
      n_data_points = 10
      n_features = 2
      X = np.random.rand(n_data_points, n_features)
      y = (X[:, 0] > X[:, 1]).astype(np.float32)
      dataset = dc.data.NumpyDataset(X, y)
      keras_model = tf.keras.Sequential(
          [
              tf.keras.layers.Dense(10, activation="relu"),
              tf.keras.layers.Dense(1, activation="sigmoid"),
          ]
      )
      model = dc.models.KerasModel(
          keras_model,
          dc.models.losses.BinaryCrossEntropy(),
          learning_rate=0.005,
      )
      model.fit(dataset, nb_epoch=1000)
      prediction = np.squeeze(model.predict_on_batch(X))
      assert np.array_equal(y, np.round(prediction))
      metric = dc.metrics.Metric(dc.metrics.roc_auc_score)
      generator = model.default_generator(dataset, pad_batches=False)
      scores = model.evaluate_generator(generator, [metric])
      assert scores[metric.name] > 0.9
  
file_path: deepchem/deepchem/models/tests/test_kerasmodel.py
imports: import os
  import math
  import deepchem
  import numpy
  import unittest
  import pytest
label_desc: Use image node_id 2 for calling a global function with example usage: test_overfit_sequential_model() without return types