class_docstr: This class implements a `Single Shot Object Detector <https://arxiv.org/abs/1512.02325>`_
  
  Args:
      opts: command-line arguments
      encoder (BaseImageEncoder): Encoder network (e.g., ResNet or MobileViT)
class_docstr_tok: ["This","class","implements","a","`","Single","Shot","Object","Detector","<","https",":","\/\/arxiv.org\/abs\/1512.02325",">","`","_","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
  
      def generate_anchors(height, width):
          """Generate anchors **on-the-fly** based on the input resolution."""
          anchors = []
          for output_stride in self.output_strides:
              if output_stride == -1:
                  fm_width = fm_height = 1
              else:
                  fm_width = int(math.ceil(width / output_stride))
                  fm_height = int(math.ceil(height / output_stride))
              fm_anchor = self.anchor_box_generator(
                  fm_height=fm_height,
                  fm_width=fm_width,
                  fm_output_stride=output_stride,
              )
              anchors.append(fm_anchor)
          anchors = torch.cat(anchors, dim=0)
          return anchors
  
      # GT boxes have the same shape as anchors. So, we use anchors as GT boxes
      gt_boxes = generate_anchors(height=height, width=width)
      gt_boxes = gt_boxes.unsqueeze(0).expand(batch_size, -1, -1)
  
      gt_box_labels = torch.randint(
          low=0,
          high=n_classes,
          size=(batch_size, gt_boxes.shape[1]),
          dtype=torch.long,
      )
  
      img_tensor = torch.randn(
          batch_size, img_channels, height, width, dtype=torch.float
      )
      labels = {
          "box_labels": gt_box_labels,
          "box_coordinates": gt_boxes,
      }
  
      return {"samples": img_tensor, "targets": labels}
  
imports: import argparse
  import math
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from torch import Tensor, nn
  from torchvision.ops import batched_nms
  from cvnets.anchor_generator import build_anchor_generator
  from cvnets.layers import AdaptiveAvgPool2d, ConvLayer2d, SeparableConv2d
  from cvnets.matcher_det import build_matcher
  from cvnets.misc.init_utils import initialize_conv_layer
  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.modules import SSDHead
  from utils import logger
  from utils.common_utils import is_coreml_conversion
label_desc: Use image node_id 10 for calling the SingleShotMaskDetector obj's underlying member method code with example usage: obj.dummy_input_and_label(batch_size) and returns: dict, anchors