code: def imdb_raw():
      max_features = 20000
      index_offset = 3  # word index offset
  
      (x_train, y_train), (
          x_test,
          y_test,
      ) = tf.keras.datasets.imdb.load_data(
          num_words=max_features, index_from=index_offset
      )
      x_train = x_train
      y_train = y_train.reshape(-1, 1)
      x_test = x_test
      y_test = y_test.reshape(-1, 1)
  
      word_to_id = tf.keras.datasets.imdb.get_word_index()
      word_to_id = {
          k: (v + index_offset) for k, v in word_to_id.items()
      }
      word_to_id["<PAD>"] = 0
      word_to_id["<START>"] = 1
      word_to_id["<UNK>"] = 2
  
      id_to_word = {value: key for key, value in word_to_id.items()}
      x_train = list(
          map(
              lambda sentence: " ".join(
                  id_to_word[i] for i in sentence
              ),
              x_train,
          )
      )
      x_test = list(
          map(
              lambda sentence: " ".join(
                  id_to_word[i] for i in sentence
              ),
              x_test,
          )
      )
      x_train = np.array(x_train, dtype=str)
      x_test = np.array(x_test, dtype=str)
      return (x_train, y_train), (x_test, y_test)
  
imports: import numpy
  import tensorflow
  from tensorflow import keras
  from clearml import Task
label_desc: Use image node_id 1 for calling a global function with example usage: imdb_raw() and returns: x_train,  y_train,  x_test,  y_test