text: from models.Unet import Unet
  import tensorflow as tf
  from losses.Pixelwise_weighted_loss import Pixelwise_weighted_loss
  from losses.Dice_loss import Dice_loss
  class Import_parameters:
      def __init__(self):
          print("import parameters init")
  
      def get_parameters(self):
          models = {1: Unet()}
          losses = {1: Pixelwise_weighted_loss().compute_loss,
                    2: Dice_loss().compute_loss}
          optimizers = {1: tf.train.GradientDescentOptimizer}
  
          prompt_msg = {"model": "Enter the model to execute. Enter 1 for Unet",
                        "loss": "Enter the loss function.  Enter 1 for Weighted loss; 2 for Dice Loss",
                        "optimizer": "Enter the optimizer. Enter 1 for SGD Optimizer",
                        "learning_rate":"Enter the learning rate between 0 and 1",
                        "weights_initializer":"Enter the weight initialization method. Enter 1 for Guassian-paper; 2 for xavier"
                        }
  
          model_opt = int(input(prompt_msg['model']))
          if model_opt not in models:
              raise Exception("Please enter valid model name")
          else:
              model = models[model_opt]
  
          loss_opt = int(input(prompt_msg['loss']))
          if loss_opt not in losses:
              raise Exception("Please enter the valid loss function")
          else:
              loss = losses[loss_opt]
              model.loss = loss
  
          optimizer_opt = int(input(prompt_msg['optimizer']))
          if optimizer_opt not in optimizers:
              raise Exception("Please enter the valid optimizer")
          else:
              optimizer = optimizers[optimizer_opt]
  
          learning_rate_opt = float(input(prompt_msg['learning_rate']))
          if learning_rate_opt > 0.0 and learning_rate_opt < 1.0:
              lr = tf.train.exponential_decay(learning_rate_opt, tf.Variable(0, trainable=False),
                                             10, 0.8, staircase=True)
              model.optimizer = optimizer(lr)
          else:
              raise Exception("Please enter valid learning rate")
              
          #int(input(prompt_msg['weights_initializer']))
  
          return model
  
  if __name__=='__main__':
      model = Import_parameters().get_parameters()
      print("sucess")