input: Write a python function to build and train a TensorFlow model using height and weight as input features.
  assert 'Dense' in build_and_train_model().to_json()
  assert 'Adam' in build_and_train_model().optimizer.get_config()['name']
  assert build_and_train_model().loss == 'sparse_categorical_crossentropy'
output: import tensorflow as tf
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  
  def build_and_train_model():
      # Define the model
      model = Sequential([
          Dense(10, activation='relu', input_shape=(2,)),
          Dense(10, activation='relu'),
          Dense(3, activation='softmax')
      ])
      
      # Compile the model
      model.compile(optimizer='adam', 
                    loss='sparse_categorical_crossentropy', 
                    metrics=['accuracy'])
      
      # Dummy data for height and weight as input features and dummy labels
      X_train = [[1.75, 70], [1.80, 80], [1.65, 60], [1.70, 65]]
      y_train = [0, 1, 2, 1]
      
      # Train the model
      model.fit(X_train, y_train, epochs=5, verbose=0)
      
      return model