docstr: Entry point for default handler. It takes the data from the input request and returns
     the predicted outcome for the input. This method is a modified variant from the BaseHandler.
     It calls the MicroBatching handle method instead of running the single processing steps.
  
  Args:
      data (list): The input data that needs to be made a prediction request on.
      context (Context): It is a JSON Object containing information pertaining to
                         the model artifacts parameters.
  
  Returns:
      list : Returns a list of dictionary with the predicted response.
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code: def __call__(self, data, context):
      """Entry point for default handler. It takes the data from the input request and returns
         the predicted outcome for the input. This method is a modified variant from the BaseHandler.
         It calls the MicroBatching handle method instead of running the single processing steps.
  
      Args:
          data (list): The input data that needs to be made a prediction request on.
          context (Context): It is a JSON Object containing information pertaining to
                             the model artifacts parameters.
  
      Returns:
          list : Returns a list of dictionary with the predicted response.
      """
  
      # It can be used for pre or post processing if needed as additional request
      # information is available in context
      start_time = time.time()
  
      self.handler.context = context
      metrics = self.handler.context.metrics
  
      is_profiler_enabled = os.environ.get(
          "ENABLE_TORCH_PROFILER", None
      )
      if is_profiler_enabled:
          if PROFILER_AVAILABLE:
              output, _ = self.handler._infer_with_profiler(data=data)
          else:
              raise RuntimeError(
                  "Profiler is enabled but current version of torch does not support."
                  "Install torch>=1.8.1 to use profiler."
              )
      else:
          if self.handler._is_describe():
              output = [self.handler.describe_handle()]
          elif self.handler._is_explain():
              data_preprocess = self.handler.preprocess(data)
              output = self.handler.explain_handle(
                  data_preprocess, data
              )
          else:
              output = self.handle(data)
  
      stop_time = time.time()
      metrics.add_time(
          "HandlerTime",
          round((stop_time - start_time) * 1000, 2),
          None,
          "ms",
      )
      return output
  
imports: import os
  import queue
  import threading
  import time
  from copy import copy
  from dataclasses import dataclass
  from typing import Dict
label_desc: Use image node_id 10 for calling the MicroBatching obj's underlying member method code with example usage: obj.__call__(data, context) and returns: output