class_docstr: Your Python model must use the same class name. Every Python model
  that is created must have "TritonPythonModel" as the class name.
docstr: `execute` must be implemented in every Python model. `execute`
  function receives a list of pb_utils.InferenceRequest as the only
  argument. This function is called when an inference is requested
  for this model. Depending on the batching configuration (e.g. Dynamic
  Batching) used, `requests` may contain multiple requests. Every
  Python model, must create one pb_utils.InferenceResponse for every
  pb_utils.InferenceRequest in `requests`. If there is an error, you can
  set the error argument when creating a pb_utils.InferenceResponse.
  Parameters
  ----------
  requests : list
    A list of pb_utils.InferenceRequest
  Returns
  -------
  list
    A list of pb_utils.InferenceResponse. The length of this list must
    be the same as `requests`
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code: def execute(self, requests):
      """`execute` must be implemented in every Python model. `execute`
      function receives a list of pb_utils.InferenceRequest as the only
      argument. This function is called when an inference is requested
      for this model. Depending on the batching configuration (e.g. Dynamic
      Batching) used, `requests` may contain multiple requests. Every
      Python model, must create one pb_utils.InferenceResponse for every
      pb_utils.InferenceRequest in `requests`. If there is an error, you can
      set the error argument when creating a pb_utils.InferenceResponse.
      Parameters
      ----------
      requests : list
        A list of pb_utils.InferenceRequest
      Returns
      -------
      list
        A list of pb_utils.InferenceResponse. The length of this list must
        be the same as `requests`
      """
      responses = []
      for request in requests:
          data = pb_utils.get_input_tensor_by_name(
              request, self.input_names[0]
          )
          data = data.as_numpy()
          outputs, im_info = self.preprocess_.run(data)
  
          # PaddleSeg preprocess has two outputs
          dlpack_tensor = outputs[0].to_dlpack()
          output_tensor_0 = pb_utils.Tensor.from_dlpack(
              self.output_names[0], dlpack_tensor
          )
          output_tensor_1 = pb_utils.Tensor(
              self.output_names[1],
              np.array([im_info], dtype=np.object_),
          )
          inference_response = pb_utils.InferenceResponse(
              output_tensors=[output_tensor_0, output_tensor_1]
          )
          responses.append(inference_response)
      return responses
  
file_path: PaddleSeg/deploy/fastdeploy/semantic_segmentation/serving/fastdeploy_serving/models/preprocess/1/model.py
imports: import json
  import numpy
  import os
  import fastdeploy
  import triton_python_backend_utils
label_desc: Use image node_id 2 for calling the TritonPythonModel obj's underlying member method code with example usage: obj.execute(requests) and returns: responses