code: def load(self, kwargs):
      super(KerasModel, self).load(kwargs)
  
      # Basic Keras Model Params
      add_parameter(self, kwargs, "output_activation", True)
      add_parameter(self, kwargs, "class_weights", True)
  
      # Specific Cost Function Params
      add_parameter(
          self, kwargs, "categorical_weighting", {0: 0.1, 1: 3.0}
      )
  
      self.keras_optimizer_dict = {
          "Nadam": Nadam,
          "Adam": Adam,
          "SGD": SGD,
          "RMSprop": RMSprop,
          "Adagrad": Adagrad,
          "Adamax": Adamax,
          "Adadelta": Adadelta,
      }
  
      if self.input_tensor is None:
          self.inputs = Input(self.input_shape)
      else:
          self.inputs = self.input_tensor
  
imports: from keras.engine import Input, Model
  from keras.layers import Activation, Lambda
  from keras.layers.merge import concatenate
  from keras.optimizers import Nadam, SGD, Adam, RMSprop, Adagrad, Adamax, Adadelta
  from keras import backend
  from deepneuro.models.cost_functions import dice_coef_loss, dice_coef
  from deepneuro.models.model import DeepNeuroModel
  from deepneuro.utilities.visualize import check_data
  from deepneuro.utilities.util import add_parameter
  from deepneuro.models.callbacks import get_callbacks
  from deepneuro.models.cost_functions import WeightedCategoricalCrossEntropy
label_desc: Use image node_id 1 for calling the KerasModel obj's underlying member method code with example usage: obj.load(kwargs) without return types