class_docstr: This class implements a `Mask RCNN style object detector <https://arxiv.org/abs/1703.06870>`
  
  Args:
      opts: command-line arguments
      encoder (BaseImageEncoder): Encoder network (e.g., ResNet or MobileViT)
class_docstr_tok: ["This","class","implements","a","`","Mask","RCNN","style","object","detector","<","https",":","\/\/arxiv.org\/abs\/1703.06870",">","`","Args",":","opts",":","command-line","arguments","encoder","(","BaseImageEncoder",")",":","Encoder","network","(","e.g.",",","ResNet","or","MobileViT",")"]
docstr: Predict the bounding boxes given an image tensor
docstr_tok: ["Predict","the","bounding","boxes","given","an","image","tensor"]
code: def predict(self, x: Tensor, *args, **kwargs) -> DetectionPredTuple:
      """Predict the bounding boxes given an image tensor"""
      assert (
          isinstance(x, Tensor) and x.ndim == 4
      ), "Expected 4D tensor as an input"
  
      bsz, channels, in_height, in_width = x.shape
      if bsz != 1:
          logger.error(
              "Prediction is supported with a batch size of 1 in {}".format(
                  self.__class__.__name__
              )
          )
  
      self.reset_generalized_rcnn_transform(
          height=in_height, width=in_width
      )
  
      outputs = self.model(x)
  
      if isinstance(outputs, List) and len(outputs) == 1:
          outputs = outputs[0]
  
      if isinstance(outputs, Dict) and {
          "boxes",
          "labels",
          "scores",
      }.issubset(outputs.keys()):
          # resize the boxes
          outputs["boxes"][:, 0::2] /= in_width
          outputs["boxes"][:, 1::2] /= in_height
  
          # predicted masks are in [N, 1, H, W] format
          # for evaluation, we need them in [N, H, W] format
          masks = outputs["masks"]
          # [N, 1, H, W] --> [N, H, W]
          masks = masks.squeeze(1)
  
          detections = DetectionPredTuple(
              labels=outputs["labels"],
              scores=outputs["scores"],
              boxes=outputs["boxes"],
              masks=masks,
          )
          return detections
      else:
          logger.error(
              "Output should be a dict with boxes, scores, and labels as keys. Got: {}".format(
                  type(outputs)
              )
          )
  
imports: import argparse
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from torch import Tensor, nn
  from torch.nn import functional
  from torchvision.models.detection.anchor_utils import AnchorGenerator
  from torchvision.models.detection.mask_rcnn import MaskRCNN
  from torchvision.ops import MultiScaleRoIAlign
  from cvnets import parameter_list
  from cvnets.layers import ConvLayer2d, Identity
  from cvnets.models import MODEL_REGISTRY
  from cvnets.models.classification.base_image_encoder import BaseImageEncoder
  from cvnets.models.detection import DetectionPredTuple
  from cvnets.models.detection.base_detection import BaseDetection
  from cvnets.models.detection.utils.rcnn_utils import FastRCNNConvFCHead, FastRCNNPredictor, MaskRCNNHeads, MaskRCNNPredictor, RPNHead
  from utils import logger
label_desc: Use image node_id 9 for calling the MaskRCNNDetector obj's underlying member method code with example usage: obj.predict(x) and returns: detections