class_docstr: Model using ResNet50 to predict single 3D oriented bounding box.
code: def __init__(self, is_training=False, output_dimension=30):
      super(SingleObjectModel, self).__init__()
  
      feature_backbone = tf.keras.applications.ResNet50(
          weights=None, input_shape=(256, 256, 3), include_top=False
      )
      feature_backbone.trainable = True
      inputs = tf.keras.Input(shape=(256, 256, 3))
      x = feature_backbone(inputs, training=is_training)
      x = tf.keras.layers.GlobalAveragePooling2D()(x)
      x = tf.keras.layers.Dense(256, activation="sigmoid")(x)
      outputs = tf.keras.layers.Dense(output_dimension)(x)
      self.network = tf.keras.Model(inputs, outputs)
      self.huber_loss = tf.keras.losses.Huber(
          reduction=tf.keras.losses.Reduction.NONE
      )
  
imports: import math
  import tensorflow
label_desc: Use image node_id 1 to create a new SingleObjectModel object  with example: obj = SingleObjectModel(is_training, output_dimension)