input: Write a function to create a deep learning model for text classification.
  assert deep_learning_model(X_train, y_train).summary() is not None
  assert isinstance(deep_learning_model(X_train, y_train), Sequential)
  assert deep_learning_model(X_train, y_train).loss == 'categorical_crossentropy'
output: from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense, Embedding, LSTM
  from tensorflow.keras.utils import to_categorical
  from tensorflow.keras.preprocessing.sequence import pad_sequences
  
  def deep_learning_model(X_train, y_train):
      max_words = 10000
      max_len = 100
      
      # Preprocessing
      X_train = pad_sequences(X_train, maxlen=max_len)
      y_train = to_categorical(y_train)
      
      # Building the model
      model = Sequential()
      model.add(Embedding(max_words, 128, input_length=max_len))
      model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
      model.add(Dense(len(y_train[0]), activation='softmax'))
      
      # Compiling the model
      model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
      
      return model