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.
class_docstr_tok: ["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","."]
code: def __init__(self, processors):
      self.processors = []
      # iterate over all given processors and save them
      for processor in processors:
          # wrap lists and tuples as a SequentialProcessor
          if isinstance(processor, (list, tuple)):
              processor = SequentialProcessor(processor)
          # save the processors
          self.processors.append(processor)
  
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 1 to create a new SequentialProcessor object from inherited base classes: MutableSequence, Processor with example: obj = SequentialProcessor(processors)