code: def initialize(self, ctx):
      super().initialize(ctx)
  
      parallelism = ctx.model_yaml_config.get("micro_batching", {}).get(
          "parallelism", None
      )
      if parallelism:
          logger.info(
              f"Setting micro batching parallelism  from model_config_yaml: {parallelism}"
          )
          self.handle.parallelism = parallelism
  
      micro_batch_size = ctx.model_yaml_config.get(
          "micro_batching", {}
      ).get("micro_batch_size", 1)
      logger.info(f"Setting micro batching size: {micro_batch_size}")
      self.handle.micro_batch_size = micro_batch_size
  
imports: import logging
  from ts.handler_utils.micro_batching import MicroBatching
  from ts.torch_handler.image_classifier import ImageClassifier
label_desc: Use image node_id 2 for calling the MicroBatchingHandler obj's underlying member method code with example usage: obj.initialize(ctx) without return types