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: Create dummy input and labels for CI/CD purposes.
docstr_tok: ["Create","dummy","input","and","labels","for","CI\/CD","purposes","."]
code: def dummy_input_and_label(self, batch_size: int) -> Dict:
      """Create dummy input and labels for CI/CD purposes."""
      img_channels = 3
      height = 320
      width = 320
      n_classes = 80
  
      # GT boxes have the same shape as anchors. So, we use anchors as GT boxes
      n_boxes = 1
  
      gt_boxes = torch.tensor([2, 20, 3, 40]).reshape(-1, 4).float()
      gt_box_labels = torch.randint(
          low=0,
          high=n_classes,
          size=(n_boxes,),
          dtype=torch.long,
      )
  
      img_tensor = torch.randn(
          img_channels, height, width, dtype=torch.float
      )
      labels = {
          "box_labels": gt_box_labels,
          "box_coordinates": gt_boxes,
      }
  
      return {
          "samples": {
              "image": [img_tensor] * batch_size,
              "label": [
                  {
                      "labels": gt_box_labels,
                      "boxes": gt_boxes,
                      "masks": torch.zeros(
                          1, height, width, dtype=torch.long
                      ),
                  }
              ]
              * batch_size,
          },
          "targets": labels,
      }
  
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 10 for calling the MaskRCNNDetector obj's underlying member method code with example usage: obj.dummy_input_and_label(batch_size) and returns: dict