class_docstr: This is the image classification class for custom models trained with the 'ClassificationModelTrainer' class. It provides support for 4 different models which are:
  ResNet50, MobileNetV2, DenseNet121 and Inception V3. After instantiating this class, you can set it's properties and
  make image classification using it's pre-defined functions.
  
  The following functions are required to be called before a classification can be made
  * setModelPath() , path to your custom model
  * setJsonPath , , path to your custom model's corresponding JSON file
  * At least of of the following and it must correspond to the model set in the setModelPath()
  [setModelTypeAsMobileNetV2(), setModelTypeAsResNet50(), setModelTypeAsDenseNet121, setModelTypeAsInceptionV3]
  * loadModel() [This must be called once only before making a classification]
  
  Once the above functions have been called, you can call the classifyImage() function of the classification instance
  object at anytime to predict an image.
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docstr: 'setJsonPath()'
  
  :param model_path:
  :return:
docstr_tok: ["'setJsonPath","(",")","'",":","param","model_path",":",":","return",":"]
code: def setJsonPath(self, model_json):
      """
      'setJsonPath()'
  
      :param model_path:
      :return:
      """
      self.jsonPath = model_json
  
imports: import tensorflow
  from PIL import Image
  import time
  import numpy
  import os
  import warnings
  from matplotlib.cbook import deprecated
  import json
label_desc: Use image node_id 3 for calling the CustomImageClassification obj's underlying member method code with example usage: obj.setJsonPath(model_json) without return types