input: Write a python function to build and train a simple neural network using TensorFlow to classify numbers from 0 to 100 into 10 classes.
  assert train_neural_network(data, labels) is not None
output: def train_neural_network(data, labels):
      import tensorflow as tf
      from tensorflow.keras import layers, models
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import LabelBinarizer
  
      # Binarize the labels
      lb = LabelBinarizer()
      labels = lb.fit_transform(labels)
  
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)
      
      # Define the model
      model = models.Sequential()
      model.add(layers.Dense(64, activation='relu', input_shape=(data.shape[1],)))
      model.add(layers.Dense(32, activation='relu'))
      model.add(layers.Dense(10, activation='softmax'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
  
      # Train the model
      model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))
  
      return model