class_docstr: Processor class for sequential processing of data.
  
  Parameters
  ----------
  processors : list
       Processor instances to be processed sequentially.
  
  Notes
  -----
  If the `processors` list contains lists or tuples, these get wrapped as a
  SequentialProcessor itself.
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docstr: Process the data sequentially with the defined processing chain.
  
  Parameters
  ----------
  data : depends on the first processor of the processing chain
      Data to be processed.
  kwargs : dict, optional
      Keyword arguments for processing.
  
  Returns
  -------
  depends on the last processor of the processing chain
      Processed data.
code: def process(self, data, **kwargs):
      """
      Process the data sequentially with the defined processing chain.
  
      Parameters
      ----------
      data : depends on the first processor of the processing chain
          Data to be processed.
      kwargs : dict, optional
          Keyword arguments for processing.
  
      Returns
      -------
      depends on the last processor of the processing chain
          Processed data.
  
      """
      # sequentially process the data
      for processor in self.processors:
          data = _process((processor, data, kwargs))
      return data
  
imports: from __future__ import absolute_import, division, print_function
  import argparse
  import itertools
  import multiprocessing
  import os
  import sys
  from collections.abc import MutableSequence
  import numpy
  from .utils import integer_types
label_desc: Use image node_id 9 for calling the SequentialProcessor obj's underlying member method code with example usage: obj.process(data) and returns: data