parameters: (self, how_many, model_settings, mode)
return_statement: return data, labels
docstring: Retrieve sample data for the given partition, with no transformations.
  
      Args:
        how_many: Desired number of samples to return. -1 means the entire
          contents of this partition.
        model_settings: Information about the current model being trained.
        mode: Which partition to use, must be 'training', 'validation', or
          'testing'.
  
      Returns:
        List of sample data for the samples, and list of labels in one-hot form.
docstring_summary: Retrieve sample data for the given partition, with no transformations.
docstring_tokens: Retrieve sample data for the given partition with no transformations .
function: def get_unprocessed_data(self, how_many, model_settings, mode):
      """Retrieve sample data for the given partition, with no transformations.
  
      Args:
        how_many: Desired number of samples to return. -1 means the entire
          contents of this partition.
        model_settings: Information about the current model being trained.
        mode: Which partition to use, must be 'training', 'validation', or
          'testing'.
  
      Returns:
        List of sample data for the samples, and list of labels in one-hot form.
      """
      candidates = self.data_index[mode]
      if how_many == -1:
        sample_count = len(candidates)
      else:
        sample_count = how_many
      desired_samples = model_settings['desired_samples']
      words_list = self.words_list
      data = np.zeros((sample_count, desired_samples))
      labels = []
      with tf.Session(graph=tf.Graph()) as sess:
        wav_filename_placeholder = tf.placeholder(tf.string, [])
        wav_loader = io_ops.read_file(wav_filename_placeholder)
        wav_decoder = contrib_audio.decode_wav(
            wav_loader, desired_channels=1, desired_samples=desired_samples)
        foreground_volume_placeholder = tf.placeholder(tf.float32, [])
        scaled_foreground = tf.multiply(wav_decoder.audio,
                                        foreground_volume_placeholder)
        for i in range(sample_count):
          if how_many == -1:
            sample_index = i
          else:
            sample_index = np.random.randint(len(candidates))
          sample = candidates[sample_index]
          input_dict = {wav_filename_placeholder: sample['file']}
          if sample['label'] == SILENCE_LABEL:
            input_dict[foreground_volume_placeholder] = 0
          else:
            input_dict[foreground_volume_placeholder] = 1
          data[i, :] = sess.run(scaled_foreground, feed_dict=input_dict).flatten()
          label_index = self.word_to_index[sample['label']]
          labels.append(words_list[label_index])
      return data, labels
function_tokens: def get_unprocessed_data ( self , how_many , model_settings , mode ) : candidates = self . data_index [ mode ] if how_many == - 1 : sample_count = len ( candidates ) else : sample_count = how_many desired_samples = model_settings [ 'desired_samples' ] words_list = self . words_list data = np . zeros ( ( sample_count , desired_samples ) ) labels = [ ] with tf . Session ( graph = tf . Graph ( ) ) as sess : wav_filename_placeholder = tf . placeholder ( tf . string , [ ] ) wav_loader = io_ops . read_file ( wav_filename_placeholder ) wav_decoder = contrib_audio . decode_wav ( wav_loader , desired_channels = 1 , desired_samples = desired_samples ) foreground_volume_placeholder = tf . placeholder ( tf . float32 , [ ] ) scaled_foreground = tf . multiply ( wav_decoder . audio , foreground_volume_placeholder ) for i in range ( sample_count ) : if how_many == - 1 : sample_index = i else : sample_index = np . random . randint ( len ( candidates ) ) sample = candidates [ sample_index ] input_dict = { wav_filename_placeholder : sample [ 'file' ] } if sample [ 'label' ] == SILENCE_LABEL : input_dict [ foreground_volume_placeholder ] = 0 else : input_dict [ foreground_volume_placeholder ] = 1 data [ i , : ] = sess . run ( scaled_foreground , feed_dict = input_dict ) . flatten ( ) label_index = self . word_to_index [ sample [ 'label' ] ] labels . append ( words_list [ label_index ] ) return data , labels