docstr: Take the noise data as an input tensor, pass it to the model and collect the output tensor.
docstr_tok: ["Take","the","noise","data","as","an","input","tensor",",","pass","it","to","the","model","and","collect","the","output","tensor","."]
code: def inference(self, preprocessed_data, *args, **kwargs):
      """
      Take the noise data as an input tensor, pass it to the model and collect the output tensor.
      """
      input_batch = torch.cat(
          tuple(map(lambda d: d["input"], preprocessed_data)), 0
      )
      with torch.no_grad():
          image_tensor = self.dcgan_model.test(
              input_batch, getAvG=True, toCPU=True
          )
      output_batch = torch.split(
          image_tensor,
          tuple(
              map(lambda d: d["number_of_images"], preprocessed_data)
          ),
      )
      return output_batch
  
file_path: serve/examples/dcgan_fashiongen/dcgan_fashiongen_handler.py
imports: import os
  import zipfile
  import torch
  from io import BytesIO
  from torchvision.utils import save_image
  from ts.torch_handler.base_handler import BaseHandler
label_desc: Use image node_id 4 for calling the ModelHandler obj's underlying member method code with example usage: obj.inference(preprocessed_data) and returns: output_batch