parameters: (x, batch_size=None)
return_statement: return [x]
docstring: Returns an iterable for feeding into predict step.
  
    Args:
      x: numpy, pandas, Dask array or iterable.
      batch_size: Size of batches to split data into.
        If `None`, returns one batch of full size.
  
    Returns:
      List or iterator of parts of data to predict on.
  
    Raises:
      ValueError: if `batch_size` <= 0.
docstring_summary: Returns an iterable for feeding into predict step.
docstring_tokens: Returns an iterable for feeding into predict step .
function: def setup_predict_data_feeder(x, batch_size=None):
    """Returns an iterable for feeding into predict step.
  
    Args:
      x: numpy, pandas, Dask array or iterable.
      batch_size: Size of batches to split data into.
        If `None`, returns one batch of full size.
  
    Returns:
      List or iterator of parts of data to predict on.
  
    Raises:
      ValueError: if `batch_size` <= 0.
    """
    if HAS_DASK:
      x = extract_dask_data(x)
    if HAS_PANDAS:
      x = extract_pandas_data(x)
    if _is_iterable(x):
      return _batch_data(x, batch_size)
    if len(x.shape) == 1:
      x = np.reshape(x, (-1, 1))
    if batch_size is not None:
      if batch_size <= 0:
        raise ValueError('Invalid batch_size %d.' % batch_size)
      n_batches = int(math.ceil(float(len(x)) / batch_size))
      return [x[i * batch_size:(i + 1) * batch_size] for i in xrange(n_batches)]
    return [x]
function_tokens: def setup_predict_data_feeder ( x , batch_size = None ) : if HAS_DASK : x = extract_dask_data ( x ) if HAS_PANDAS : x = extract_pandas_data ( x ) if _is_iterable ( x ) : return _batch_data ( x , batch_size ) if len ( x . shape ) == 1 : x = np . reshape ( x , ( - 1 , 1 ) ) if batch_size is not None : if batch_size <= 0 : raise ValueError ( 'Invalid batch_size %d.' % batch_size ) n_batches = int ( math . ceil ( float ( len ( x ) ) / batch_size ) ) return [ x [ i * batch_size : ( i + 1 ) * batch_size ] for i in xrange ( n_batches ) ] return [ x ]