original_docstring: 
          Flattens the training paths
          Input : class_names
          Output: X - a list of the flattened training data paths
                  y - a list of the class labels (index in provided list) for each path
          
docstring: Flattens the training paths
  Input : class_names
  Output: X - a list of the flattened training data paths
  y - a list of the class labels (index in provided list) for each path
docstring_tokens: Flattens the training paths Input : class_names Output : X - a list of the flattened training data paths y - a list of the class labels ( index in provided list ) for each path
code: def flattened_train_paths(self, class_names):
          """
          Flattens the training paths
          Input : class_names
          Output: X - a list of the flattened training data paths
                  y - a list of the class labels (index in provided list) for each path
          """
          num_paths = sum([len(self.image_fnames['train'][classname]) for classname in self.image_fnames['train'].keys()])
          paths = []
          labels = []
          for class_index, class_name in enumerate(class_names):
              paths += self.image_fnames['train'][class_name]
              labels += len(self.image_fnames['train'][class_name]) * [class_index]
          return paths, labels
code_tokens: def flattened_train_paths ( self , class_names ) : num_paths = sum ( [ len ( self . image_fnames [ 'train' ] [ classname ] ) for classname in self . image_fnames [ 'train' ] . keys ( ) ] ) paths = [ ] labels = [ ] for class_index , class_name in enumerate ( class_names ) : paths += self . image_fnames [ 'train' ] [ class_name ] labels += len ( self . image_fnames [ 'train' ] [ class_name ] ) * [ class_index ] return paths , labels
short_docstring: Flattens the training paths
  Input : class_names
  Output: X - a list of the flattened training data paths
  y - a list of the class labels (index in provided list) for each path
short_docstring_tokens: Flattens the training paths Input : class_names Output : X - a list of the flattened training data paths y - a list of the class labels ( index in provided list ) for each path
comment: """
          Flattens the training paths
          Input : class_names
          Output: X - a list of the flattened training data paths
                  y - a list of the class labels (index in provided list) for each path
          """