class_docstr: BaseApplication represents an interface.
  
  Each application ``type_str`` should support to use
  the standard training and inference driver.
class_docstr_tok: ["BaseApplication","represents","an","interface",".","Each","application","``","type_str","``","should","support","to","use","the","standard","training","and","inference","driver","."]
docstr: this function initialise self.readers
  
  :param data_param: input modality specifications
  :param task_param: contains task keywords for grouping data_param
  :param data_partitioner:
                     specifies train/valid/infer splitting if needed
  :return:
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code: def initialise_dataset_loader(
      self, data_param=None, task_param=None, data_partitioner=None
  ):
      """
      this function initialise self.readers
  
      :param data_param: input modality specifications
      :param task_param: contains task keywords for grouping data_param
      :param data_partitioner:
                         specifies train/valid/infer splitting if needed
      :return:
      """
      raise NotImplementedError
  
imports: import os
  from argparse import Namespace
  import tensorflow
  from six import with_metaclass
  from niftynet.engine.signal import TRAIN, INFER, EVAL
label_desc: Use image node_id 1 for calling the BaseApplication obj's underlying member method code with example usage: obj.initialise_dataset_loader(data_param, task_param, data_partitioner) without return types