code: def __init__(
      self,
      model_prefix,
      gpu_id,
      image_width=1280,
      image_height=720,
      threshold=0.05,
      use_torch=False,
  ):
      self.torch = use_torch
      if self.torch:
          assert (
              torch is not None
          ), "requires torch when `use_torch` is enabled"
      self.__new_nd_array = tvm.nd.array
      self.device_ctx = tvm.gpu(gpu_id)
      self.image_width = image_width
      self.image_height = image_height
      self.__model = self.load_model(model_prefix, self.device_ctx)
      self.threshold = threshold
      self.mean = np.array(
          [0.406 * 255, 0.456 * 255, 0.485 * 255], dtype=np.float32
      )
      self.std = np.array([1, 1, 1], dtype=np.float32)
  
file_path: gluon-cv/scripts/pose/directpose/tvm_evaluation/pose_model.py
imports: import tvm
  from tvm.contrib import graph_runtime
  from typing import List
  import cv2
  import numpy
label_desc: Use image node_id 1 to create a new PoseEstimationInferenceModel object  with example: obj = PoseEstimationInferenceModel(model_prefix, gpu_id, image_width, image_height, threshold, use_torch)