code: def __init__(
      self,
      gpu_id,
      aspect_ratio=1.0,
      data_shape=512,
      model_name="",
      use_pretrained=False,
      param_path="",
  ):
      self.ctx = mx.gpu(gpu_id)
  
      self.net = get_net(
          classes=COCODetection.CLASSES,
          ctx=self.ctx,
          model_name=model_name,
          use_pretrained=use_pretrained,
          param_path=param_path,
      )
  
      self.anchor_tensor = None
      self._anchor_image_shape = (1, 1)
      self._anchor_num = 1
  
      self.mean_mx = mx.nd.array(
          np.array([0.485, 0.456, 0.406])
      ).as_in_context(self.ctx)
      self.std_mx = mx.nd.array(
          np.array([0.229, 0.224, 0.225])
      ).as_in_context(self.ctx)
      self.ratio = aspect_ratio
      self.data_shape = data_shape
  
file_path: gluon-cv/gluoncv/model_zoo/smot/general_detector.py
imports: import logging
  import numpy
  import mxnet
  from gluoncv.data import COCODetection
  from .utils import mxnet_frame_preprocessing, timeit_context
  from .utils import remap_bboxes
  from .presets import *
label_desc: Use image node_id 1 to create a new GeneralDetector object  with example: obj = GeneralDetector(gpu_id, aspect_ratio, data_shape, model_name, use_pretrained, param_path)