docstr: Parameters
  ----------
  model_version : str, optional
      
docstr_tok: ["Parameters","--","--","--","--","--","model_version",":","str",",","optional"]
code: def load(self, kwargs):
      """Parameters
      ----------
      model_version : str, optional
  
  
      """
  
      super(KerasPreTrainedModel, self).load(kwargs)
  
      # Model Choice Parameters
      add_parameter(self, kwargs, "model_type", "inception")
      add_parameter(self, kwargs, "pretrained_weights", "imagenet")
  
      # Finetuning Parameters
      add_parameter(self, kwargs, "input_shape", None)
      add_parameter(self, kwargs, "output_classes", None)
      add_parameter(self, kwargs, "bottleneck_layers_num", None)
      add_parameter(self, kwargs, "finetuning_dense_features", 128)
  
      self.models = {
          "vgg16": VGG16,
          "vgg19": VGG19,
          "inception": InceptionV3,
          "xception": Xception,
          "resnet50": ResNet50,
      }
  
      self.output_activation = False
  
      if self.input_shape is None:
          self.include_top = False
      else:
          self.include_top = True
  
file_path: DeepNeuro/deepneuro/models/keras_pretrained.py
imports: from keras.applications import ResNet50
  from keras.applications import InceptionV3
  from keras.applications import Xception
  from keras.applications import VGG16
  from keras.applications import VGG19
  from keras.layers import Dense, GlobalAveragePooling2D
  from keras.engine import Model
  from deepneuro.models.keras_model import KerasModel
  from deepneuro.utilities.util import add_parameter
label_desc: Use image node_id 1 for calling the KerasPreTrainedModel obj's underlying member method code with example usage: obj.load(kwargs) without return types