class_docstr: Arguments:
      threshold (float): Threshold for classifier confidence score
class_docstr_tok: ["Arguments",":","threshold","(","float",")",":","Threshold","for","classifier","confidence","score"]
code: def labels(self) -> List[str]:
      return [
          "__background__",
          "person",
          "bicycle",
          "car",
          "motorcycle",
          "airplane",
          "bus",
          "train",
          "truck",
          "boat",
          "traffic light",
          "fire hydrant",
          "N/A",
          "stop sign",
          "parking meter",
          "bench",
          "bird",
          "cat",
          "dog",
          "horse",
          "sheep",
          "cow",
          "elephant",
          "bear",
          "zebra",
          "giraffe",
          "N/A",
          "backpack",
          "umbrella",
          "N/A",
          "N/A",
          "handbag",
          "tie",
          "suitcase",
          "frisbee",
          "skis",
          "snowboard",
          "sports ball",
          "kite",
          "baseball bat",
          "baseball glove",
          "skateboard",
          "surfboard",
          "tennis racket",
          "bottle",
          "N/A",
          "wine glass",
          "cup",
          "fork",
          "knife",
          "spoon",
          "bowl",
          "banana",
          "apple",
          "sandwich",
          "orange",
          "broccoli",
          "carrot",
          "hot dog",
          "pizza",
          "donut",
          "cake",
          "chair",
          "couch",
          "potted plant",
          "bed",
          "N/A",
          "dining table",
          "N/A",
          "N/A",
          "toilet",
          "N/A",
          "tv",
          "laptop",
          "mouse",
          "remote",
          "keyboard",
          "cell phone",
          "microwave",
          "oven",
          "toaster",
          "sink",
          "refrigerator",
          "N/A",
          "book",
          "clock",
          "vase",
          "scissors",
          "teddy bear",
          "hair drier",
          "toothbrush",
      ]
  
file_path: evadb/evadb/functions/fastrcnn_object_detector.py
imports: from typing import List
  import numpy
  import pandas
  from evadb.catalog.catalog_type import NdArrayType
  from evadb.functions.abstract.pytorch_abstract_function import PytorchAbstractClassifierFunction
  from evadb.functions.decorators.decorators import forward, setup
  from evadb.functions.decorators.io_descriptors.data_types import PandasDataframe, PyTorchTensor
  from evadb.utils.generic_utils import try_to_import_torch, try_to_import_torchvision
label_desc: Use image node_id 3 for calling the FastRCNNObjectDetector obj's underlying member method code with example usage: obj.labels() and returns: list