class_docstr: DenseNetHandler handler class. This handler takes an image
  and returns the name of object in that image.
class_docstr_tok: ["DenseNetHandler","handler","class",".","This","handler","takes","an","image","and","returns","the","name","of","object","in","that","image","."]
docstr: Override to customize the inference
  :param data: Torch tensor, matching the model input shape
  :return: Prediction output as Torch tensor
docstr_tok: ["Override","to","customize","the","inference",":","param","data",":","Torch","tensor",",","matching","the","model","input","shape",":","return",":","Prediction","output","as","Torch","tensor"]
code: def inference(self, data, *args, **kwargs):
      """
      Override to customize the inference
      :param data: Torch tensor, matching the model input shape
      :return: Prediction output as Torch tensor
      """
      marshalled_data = data.to(self.device)
      with torch.no_grad():
          results = self.model(marshalled_data, *args, **kwargs)
      return results
  
file_path: serve/ts/torch_handler/densenet_handler.py
imports: import inspect
  import logging
  import os
  import importlib.util
  import time
  import io
  import torch
label_desc: Use image node_id 5 for calling the DenseNetHandler obj's underlying member method code with example usage: obj.inference(data) and returns: results