text: #!/usr/bin/env/python3
  #
  # Copyright (c) Facebook, Inc. and its affiliates.
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import ast
  import logging
  
  import numpy as np
  import torch
  from ts.torch_handler.base_handler import BaseHandler
  
  logger = logging.getLogger(__name__)
  
  
  class IRISClassifierHandler(BaseHandler):
      """
      IRISClassifier handler class. This handler takes an input tensor and
      output the type of iris based on the input
      """
  
      def __init__(self):
          super(IRISClassifierHandler, self).__init__()
  
      def preprocess(self, data):
          """
          preprocessing step - Reads the input array and converts it to tensor
  
          :param data: Input to be passed through the layers for prediction
  
          :return: output - Preprocessed input
          """
  
          input_data_str = data[0].get("data")
          if input_data_str is None:
              input_data_str = data[0].get("body")
  
          input_data = input_data_str.decode("utf-8")
          input_tensor = torch.Tensor(ast.literal_eval(input_data))
          return input_tensor
  
      def postprocess(self, inference_output):
          """
          Does postprocess after inference to be returned to user
  
          :param inference_output: Output of inference
  
          :return: output - Output after post processing
          """
  
          predicted_idx = str(np.argmax(inference_output.cpu().detach().numpy()))
  
          if self.mapping:
              return [self.mapping[str(predicted_idx)]]
          return [predicted_idx]
  
  
  _service = IRISClassifierHandler()
  