Typeerror: 'nonetype' object is not callable

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Python

I'm new with python and I'm doing a project trying to classify movie posters by genre. Then, when I run main, I came up to this problem :
File "/home/.../Desktop/movies_genre_model.py", line 56, in build
num_classes = len(y_train(0))
TypeError: 'NoneType' object is not callable

What does this mean?

What I have tried:

  1  import os
  2  import time
  3  
  4  import keras
  5  from keras.layers import Conv2D, MaxPooling2D
  6  from keras.layers import Dense, Dropout, Flatten
  7  from keras.models import Sequential
  8  
  9  import movies_dataset as movies
 10  
 11  
 12  def get_kernel_dimensions(version, shape, divisor):
 13      image_width = shape[0]
 14  
 15      # original
 16      if version == 1:
 17          return 3, 3
 18  
 19      # square 10% width
 20      if version == 2:
 21          return int(0.1 * image_width / divisor), int(0.1 * image_width / divisor)
 22  
 23      # square 20% width
 24      if version == 3:
 25          return int(0.2 * image_width / divisor), int(0.2 * image_width / divisor)
 26  
 27  
 28  def build(version, min_year, max_year, genres, ratio, epochs,
 29            x_train=None, y_train=None, x_validation=None, y_validation=None):
 30      # log
 31      print()
 32      print('version:', version)
 33      print('min_year:', min_year)
 34      print('max_year:', max_year)
 35      print('genres:', genres)
 36      print('ratio:', ratio)
 37      print()
 38  
 39      # load data if not provided
 40      if x_train is None or y_train is None or x_validation is None or y_validation is None:
 41          begin = time.time()
 42          x_train = movies.load_genre_data(min_year, max_year, genres, ratio, 'train')
 43          y_train = movies.load_genre_data(min_year, max_year, genres, ratio, 'train')
 44          x_validation = movies.load_genre_data(min_year, max_year, genres, ratio, 'validation')
 45          y_validation = movies.load_genre_data(min_year, max_year, genres, ratio, 'validation')
 46          print('loaded in', (time.time() - begin) / 60, 'min.')
 47      else:
 48          print('data provided in arguments')
 49  
 50      print()
 51      print('x_train shape:', x_train.shape)
 52      print(x_train.shape[0], 'train samples')
 53      print(x_validation.shape[0], 'validation samples')
 54  
 55      # build model
 56      num_classes = len(y_train(0))
 57      kernel_dimensions1 = get_kernel_dimensions(version, x_train.shape, 1)
 58      kernel_dimensions2 = get_kernel_dimensions(version, x_train.shape, 2)
 59      print('kernel_dimensions1:', kernel_dimensions1)
 60      print('kernel_dimensions2:', kernel_dimensions2)
 61  
 62      model = Sequential([
 63          Conv2D(32, kernel_dimensions1, padding='same', input_shape=x_train.shape[1:], activation='relu'),
 64          Conv2D(32, kernel_dimensions1, activation='relu'),
 65          MaxPooling2D(pool_size=(2, 2)),
 66          Dropout(0.25),
 67  
 68          Conv2D(64, kernel_dimensions2, padding='same', activation='relu'),
 69          Conv2D(64, kernel_dimensions2, activation='relu'),
 70          MaxPooling2D(pool_size=(2, 2)),
 71          Dropout(0.25),
 72  
 73          Flatten(),
 74          Dense(512, activation='relu'),
 75          Dropout(0.5),
 76          Dense(num_classes, activation='sigmoid')
 77      ])
 78  
 79      opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
 80      model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
 81      print(model.summary())
 82  
 83      model.fit(x_train, y_train, batch_size=32, epochs=epochs, validation_data=(x_validation, y_validation))
 84  
 85      # create dir if none
 86      save_dir = os.path.join(os.getcwd(), 'saved_models')
 87      if not os.path.isdir(save_dir):
 88          os.makedirs(save_dir)
 89  
 90      # save model
 91      model_file_name = 'genres' \
 92                        + '_' + str(min_year) + '_' + str(max_year) \
 93                        + '_g' + str(len(genres)) \
 94                        + '_r' + str(ratio) \
 95                        + '_e' + str(epochs) \
 96                        + '_v' + str(version) + '.h5'
 97  
 98      model_path = os.path.join(save_dir, model_file_name)
 99      model.save(model_path)
100      print('Saved trained model at %s ' % model_path)