parameters: (self,
              batch_size,
              shuffle=False,
              num_threads=1,
              queue_capacity=None,
              min_after_dequeue=None,
              seed=None)
return_statement: return dataframe
docstring: Resize the batches in the `DataFrame` to the given `batch_size`.
  
      Args:
        batch_size: desired batch size.
        shuffle: whether records should be shuffled. Defaults to true.
        num_threads: the number of enqueueing threads.
        queue_capacity: capacity of the queue that will hold new batches.
        min_after_dequeue: minimum number of elements that can be left by a
          dequeue operation. Only used if `shuffle` is true.
        seed: passed to random shuffle operations. Only used if `shuffle` is true.
  
      Returns:
        A `DataFrame` with `batch_size` rows.
docstring_summary: Resize the batches in the `DataFrame` to the given `batch_size`.
docstring_tokens: Resize the batches in the DataFrame to the given batch_size .
function: def batch(self,
              batch_size,
              shuffle=False,
              num_threads=1,
              queue_capacity=None,
              min_after_dequeue=None,
              seed=None):
      """Resize the batches in the `DataFrame` to the given `batch_size`.
  
      Args:
        batch_size: desired batch size.
        shuffle: whether records should be shuffled. Defaults to true.
        num_threads: the number of enqueueing threads.
        queue_capacity: capacity of the queue that will hold new batches.
        min_after_dequeue: minimum number of elements that can be left by a
          dequeue operation. Only used if `shuffle` is true.
        seed: passed to random shuffle operations. Only used if `shuffle` is true.
  
      Returns:
        A `DataFrame` with `batch_size` rows.
      """
      column_names = list(self._columns.keys())
      if shuffle:
        batcher = batch.ShuffleBatch(batch_size,
                                     output_names=column_names,
                                     num_threads=num_threads,
                                     queue_capacity=queue_capacity,
                                     min_after_dequeue=min_after_dequeue,
                                     seed=seed)
      else:
        batcher = batch.Batch(batch_size,
                              output_names=column_names,
                              num_threads=num_threads,
                              queue_capacity=queue_capacity)
  
      batched_series = batcher(list(self._columns.values()))
      dataframe = type(self)()
      dataframe.assign(**(dict(zip(column_names, batched_series))))
      return dataframe
function_tokens: def batch ( self , batch_size , shuffle = False , num_threads = 1 , queue_capacity = None , min_after_dequeue = None , seed = None ) : column_names = list ( self . _columns . keys ( ) ) if shuffle : batcher = batch . ShuffleBatch ( batch_size , output_names = column_names , num_threads = num_threads , queue_capacity = queue_capacity , min_after_dequeue = min_after_dequeue , seed = seed ) else : batcher = batch . Batch ( batch_size , output_names = column_names , num_threads = num_threads , queue_capacity = queue_capacity ) batched_series = batcher ( list ( self . _columns . values ( ) ) ) dataframe = type ( self ) ( ) dataframe . assign ( * * ( dict ( zip ( column_names , batched_series ) ) ) ) return dataframe