code: def fix_get_rcnn():
      from art.estimators.estimator import (
          BaseEstimator,
          LossGradientsMixin,
      )
      from art.estimators.object_detection.object_detector import (
          ObjectDetectorMixin,
      )
  
      class DummyObjectDetector(
          ObjectDetectorMixin, LossGradientsMixin, BaseEstimator
      ):
          def __init__(self):
              self._clip_values = (0, 1)
              self.channels_first = False
              self._input_shape = None
              self._compute_loss_count = 1
  
          def loss_gradient(self, x: np.ndarray, y: None, **kwargs):
              return np.ones_like(x)
  
          def fit(
              self,
              x: np.ndarray,
              y,
              batch_size: int = 128,
              nb_epochs: int = 20,
              **kwargs,
          ):
              raise NotImplementedError
  
          def predict(
              self, x: np.ndarray, batch_size: int = 128, **kwargs
          ):
              dict_i = {
                  "boxes": np.array([[0.1, 0.2, 0.3, 0.4]]),
                  "labels": np.array([[2]]),
                  "scores": np.array([[0.8]]),
              }
              return [dict_i] * x.shape[0]
  
          @property
          def native_label_is_pytorch_format(self):
              return True
  
          @property
          def input_shape(self):
              return self._input_shape
  
          def compute_losses(
              self,
              x: np.ndarray,
              y: Union[
                  List[Dict[str, np.ndarray]],
                  List[Dict[str, "torch.Tensor"]],
              ],
          ) -> Dict[str, np.ndarray]:
              losses_dict = {
                  "loss_classifier": np.array(0.43572357, dtype=float),
                  "loss_box_reg": np.array(0.17341757, dtype=float),
                  "loss_objectness": np.array(0.02198849, dtype=float),
                  "loss_rpn_box_reg": np.array(0.03471708, dtype=float),
              }
  
              return losses_dict
  
          def compute_loss(
              self,
              x: np.ndarray,
              y: Union[
                  List[Dict[str, np.ndarray]],
                  List[Dict[str, "torch.Tensor"]],
              ],
              **kwargs,
          ) -> Union[np.ndarray, "torch.Tensor"]:
              self._compute_loss_count += 1
              loss = 0.43572357 / self._compute_loss_count
              return loss
  
      frcnn = DummyObjectDetector()
      return frcnn
  
imports: import importlib
  import json
  import logging
  import os
  import shutil
  import tempfile
  from typing import Dict, List, TYPE_CHECKING, Union
  import warnings
  import numpy
  import pytest
  import requests
  from art.data_generators import KerasDataGenerator, MXDataGenerator, PyTorchDataGenerator, TensorFlowDataGenerator, TensorFlowV2DataGenerator
  from art.defences.preprocessor import FeatureSqueezing, JpegCompression, SpatialSmoothing
  from art.estimators.classification import KerasClassifier
  from tests.utils import ARTTestFixtureNotImplemented, get_attack_classifier_pt, get_image_classifier_kr, get_image_classifier_kr_functional, get_image_classifier_kr_tf, get_image_classifier_kr_tf_functional, get_image_classifier_kr_tf_with_wildcard, get_image_classifier_mxnet_custom_ini, get_image_classifier_pt, get_image_classifier_pt_functional, get_image_classifier_tf, get_image_classifier_hf, get_image_gan_tf_v2, get_image_generator_tf_v2, get_tabular_classifier_kr, get_tabular_classifier_pt, get_tabular_classifier_scikit_list, get_tabular_classifier_tf, load_dataset, master_seed
label_desc: Use image node_id 44 for calling a global function with example usage: fix_get_rcnn() and returns: frcnn, np, unknown, True, self, losses_dict, loss