input: 
  def train_handwritten_digit_recognizer():
      """Train a neural network model to recognize handwritten digits from 0 to 9 using the MNIST dataset.
      >>> model = train_handwritten_digit_recognizer()
      >>> model.evaluate(X_test, y_test)[1] > 0.95
      True
      """
  
output:     
      import tensorflow as tf
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense, Flatten
      from tensorflow.keras.utils import to_categorical
  
      # Load and preprocess the MNIST dataset
      (X_train, y_train), (X_test, y_test) = mnist.load_data()
      X_train = X_train / 255.0
      X_test = X_test / 255.0
      y_train = to_categorical(y_train, 10)
      y_test = to_categorical(y_test, 10)
  
      # Build the model
      model = Sequential([
          Flatten(input_shape=(28, 28)),
          Dense(128, activation='relu'),
          Dense(64, activation='relu'),
          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=5, batch_size=32, validation_split=0.1)
  
      return model
  