parameters: (data_set, images_pl, labels_pl)
return_statement: return feed_dict
docstring: Fills the feed_dict for training the given step.
  
    A feed_dict takes the form of:
    feed_dict = {
        <placeholder>: <tensor of values to be passed for placeholder>,
        ....
    }
  
    Args:
      data_set: The set of images and labels, from input_data.read_data_sets()
      images_pl: The images placeholder, from placeholder_inputs().
      labels_pl: The labels placeholder, from placeholder_inputs().
  
    Returns:
      feed_dict: The feed dictionary mapping from placeholders to values.
docstring_summary: Fills the feed_dict for training the given step.
docstring_tokens: Fills the feed_dict for training the given step .
function: def fill_feed_dict(data_set, images_pl, labels_pl):
    """Fills the feed_dict for training the given step.
  
    A feed_dict takes the form of:
    feed_dict = {
        <placeholder>: <tensor of values to be passed for placeholder>,
        ....
    }
  
    Args:
      data_set: The set of images and labels, from input_data.read_data_sets()
      images_pl: The images placeholder, from placeholder_inputs().
      labels_pl: The labels placeholder, from placeholder_inputs().
  
    Returns:
      feed_dict: The feed dictionary mapping from placeholders to values.
    """
    # Create the feed_dict for the placeholders filled with the next
    # `batch size` examples.
    images_feed, labels_feed = data_set.next_batch(FLAGS.batch_size,
                                                   FLAGS.fake_data)
    feed_dict = {
        images_pl: images_feed,
        labels_pl: labels_feed,
    }
    return feed_dict
function_tokens: def fill_feed_dict ( data_set , images_pl , labels_pl ) : # Create the feed_dict for the placeholders filled with the next # `batch size` examples. images_feed , labels_feed = data_set . next_batch ( FLAGS . batch_size , FLAGS . fake_data ) feed_dict = { images_pl : images_feed , labels_pl : labels_feed , } return feed_dict