class_docstr: Wrapper for custom entry_point
docstr: PREDICT COMMAND = {
      "command": "predict",
      "batch": [ REQUEST_INPUT ]
  }
  :param batch: list of request
  :return:
docstr_tok: ["PREDICT","COMMAND","=","{","``","command","''",":","``","predict","''",",","``","batch","''",":","[","REQUEST_INPUT","]","}",":","param","batch",":","list","of","request",":","return",":"]
code: def predict(self, batch):
      """
      PREDICT COMMAND = {
          "command": "predict",
          "batch": [ REQUEST_INPUT ]
      }
      :param batch: list of request
      :return:
  
      """
      (
          headers,
          input_batch,
          req_id_map,
      ) = Service.retrieve_data_for_inference(batch)
  
      self.context.request_ids = req_id_map
      self.context.request_processor = headers
      metrics = self.context.metrics
      metrics.request_ids = req_id_map
  
      start_time = time.time()
  
      # noinspection PyBroadException
      try:
          ret = self._entry_point(input_batch, self.context)
      except MemoryError:
          logger.error("System out of memory", exc_info=True)
          return create_predict_response(
              None, req_id_map, "Out of resources", 507
          )
      except PredictionException as e:
          logger.error("Prediction error", exc_info=True)
          return create_predict_response(
              None, req_id_map, e.message, e.error_code
          )
      except Exception as ex:  # pylint: disable=broad-except
          if "CUDA" in str(ex):
              # Handles Case A: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED (Close to OOM) &
              # Case B: CUDA out of memory (OOM)
              logger.error("CUDA out of memory", exc_info=True)
              return create_predict_response(
                  None, req_id_map, "Out of resources", 507
              )
          else:
              logger.warning(
                  "Invoking custom service failed.", exc_info=True
              )
              return create_predict_response(
                  None, req_id_map, "Prediction failed", 503
              )
  
      if not isinstance(ret, list):
          logger.warning(
              "model: %s, Invalid return type: %s.",
              self.context.model_name,
              type(ret),
          )
          return create_predict_response(
              None, req_id_map, "Invalid model predict output", 503
          )
  
      if len(ret) != len(input_batch):
          logger.warning(
              "model: %s, number of batch response mismatched, expect: %d, got: %d.",
              self.context.model_name,
              len(input_batch),
              len(ret),
          )
          return create_predict_response(
              None,
              req_id_map,
              "number of batch response mismatched",
              503,
          )
  
      duration = round((time.time() - start_time) * 1000, 2)
      metrics.add_time(PREDICTION_METRIC, duration)
  
      return create_predict_response(
          ret,
          req_id_map,
          "Prediction success",
          200,
          context=self.context,
      )
  
file_path: serve/ts/service.py
imports: import logging
  import os
  import time
  from builtins import str
  import ts
  from ts.context import Context, RequestProcessor
  from ts.protocol.otf_message_handler import create_predict_response
  from ts.utils.util import PredictionException, get_yaml_config
label_desc: Use image node_id 5 for calling the Service obj's underlying member method code with example usage: obj.predict(batch) and returns: create_predict_response, create_predict_response, create_predict_response, create_predict_response, create_predict_response, create_predict_response, create_predict_response