class_docstr: BatchProcessor Class for plug and play fit_batch & evaluate_batch
  
  During training or validation, data are divided into minibatches for processing. This
  class aims at providing hooks of training or validating on a minibatch of data. Users
  may provide customized fit_batch() and evaluate_batch() methods by inheriting from
  this class and overriding class methods.
  
  :py:class:`BatchProcessor` can be used to replace fit_batch() and evaluate_batch()
  in the base estimator class
code: def __init__(self):
      pass
  
file_path: mxnet/python/mxnet/gluon/contrib/estimator/batch_processor.py
imports: from ...utils import split_and_load
  from ....None import autograd
  from ....None import npx
label_desc: Use image node_id 1 to create a new BatchProcessor object from inherited base classes: object with example: obj = BatchProcessor()