class_docstr: The TritonClient to access model on Triton server.
  
  Args:
      model_name (`str`):
          The name of the model.
      input_names (`List[str]`):
          The list of the input names.
      output_names (`List[str]`):
          The list of the output names.
code: def __call__(self, *args, **kwargs):
      inputs = [
          self._torch_tensor_to_pbtenor(name, array)
          for array, name in zip(args, self._input_names)
      ]
  
      for name, array in kwargs.items():
          inputs.append(self._torch_tensor_to_pbtenor(name, array))
  
      inference_request = pb_utils.InferenceRequest(
          model_name=self._model_name,
          requested_output_names=self._output_names,
          inputs=inputs,
      )
  
      inference_response = inference_request.exec()
  
      if inference_response.has_error():
          raise pb_utils.TritonModelException(
              inference_response.error().message()
          )
      else:
          if len(self._output_names) == 1:
              outputs = torch.tensor(
                  pb_utils.get_output_tensor_by_name(
                      inference_response, self._output_names[0]
                  ).as_numpy()
              )
          else:
              outputs = [
                  torch.tensor(
                      pb_utils.get_output_tensor_by_name(
                          inference_response, name
                      ).as_numpy()
                  )
                  for name in self._output_names
              ]
  
      return outputs
  
imports: import torch
  from typing import List
  from towhee.serve.triton.bls.python_backend_wrapper import pb_utils
label_desc: Use image node_id 3 for calling the TritonClient obj's underlying member method code with example usage: obj.__call__() and returns: outputs