input: Write a python function to create a neural network model in Python to classify text as positive or negative.
  assert isinstance(create_model(vocab_size), tf.keras.Model)
  assert create_model(1000).layers[0].input_shape == [(None, 1000)]
  assert create_model(500).layers[-1].output_shape == (None, 1)
output: import tensorflow as tf
  from tensorflow.keras import layers
  
  def create_model(vocab_size):
      model = tf.keras.Sequential([
          layers.Input(shape=(vocab_size,)),
          layers.Embedding(input_dim=vocab_size, output_dim=64),
          layers.GlobalAveragePooling1D(),
          layers.Dense(64, activation='relu'),
          layers.Dense(1, activation='sigmoid')
      ])
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      return model