code: def __init__(
      self,
      input_h,
      input_w,
      batch_size,
      output_size,
      out_h,
      out_w,
      init_weights,
      config,
  ):
      self.gpu_policy = _utils.TensorFlowGPUPolicy()
      self.gpu_policy.start()
  
      # Converting incoming weights from shared_float_array to numpy
      for key in init_weights.keys():
          init_weights[
              key
          ] = _utils.convert_shared_float_array_to_numpy(
              init_weights[key]
          )
  
      _tf = _lazy_import_tensorflow()
      self.od_graph = _tf.Graph()
      self.config = config
      self.batch_size = batch_size
      self.grid_shape = [out_h, out_w]
      self.num_classes = int(
          _utils.convert_shared_float_array_to_numpy(
              config["num_classes"]
          )
      )
      self.anchors = [
          (1.0, 2.0),
          (1.0, 1.0),
          (2.0, 1.0),
          (2.0, 4.0),
          (2.0, 2.0),
          (4.0, 2.0),
          (4.0, 8.0),
          (4.0, 4.0),
          (8.0, 4.0),
          (8.0, 16.0),
          (8.0, 8.0),
          (16.0, 8.0),
          (16.0, 32.0),
          (16.0, 16.0),
          (32.0, 16.0),
      ]
      self.num_anchors = len(self.anchors)
      self.output_size = output_size
      self.sess = _tf.Session(graph=self.od_graph)
      with self.od_graph.as_default():
          self.init_object_detector_graph(
              input_h, input_w, init_weights
          )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import numpy
  from .._tf_model import TensorFlowModel
  from turicreate._deps.minimal_package import _minimal_package_import_check
  import turicreate.toolkits._tf_utils
label_desc: Use image node_id 1 to create a new ODTensorFlowModel object from inherited base classes: TensorFlowModel with example: obj = ODTensorFlowModel(input_h, input_w, batch_size, output_size, out_h, out_w, init_weights, config)