class_docstr: The main explainer class that implements functions for computation of sample-wise weights from probe confidences of different layers from
  the complex model and then retrains the simple model with those weights. Implements the technique in the following reference: [#]_.
  
  References:
      .. [#] `Dhurandhar, Shanmugam, Luss, Olsen. Improving Simple Models with Confidence Profiles. NeurIPS 2018 <https://arxiv.org/abs/1807.07506>`_
docstr: Set parameters for the explainer.
docstr_tok: ["Set","parameters","for","the","explainer","."]
code: def set_params(self, *argv, **kwargs):
      """
      Set parameters for the explainer.
      """
      print("TBD: Implement set params in DIPVAEExplainer")
  
file_path: AIX360/aix360/algorithms/profwt/profwt.py
imports: from aix360.algorithms.gwbe import GlobalWBExplainer
  import numpy
  import keras
  from keras.callbacks import ModelCheckpoint
  from keras.layers import Dense, Conv2D, BatchNormalization, Activation
  from keras.layers import AveragePooling2D, Input, Flatten
  from keras.optimizers import Adam
  from keras.models import Model
  from keras import backend
  from aix360.algorithms.profwt.sample_weight_from_probes import prof_weight_compute
label_desc: Use image node_id 2 for calling the ProfweightExplainer obj's underlying member method code with example usage: obj.set_params() without return types