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",")"]
code: def ssd_forward(
      self,
      end_points: Dict[str, Tensor],
      device: Optional[torch.device] = torch.device("cpu"),
      *args,
      **kwargs,
  ) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[Tensor, ...]]:
      locations = []
      confidences = []
      anchors = []
  
      for os, ssd_head in zip(self.output_strides, self.ssd_heads):
          x = end_points["os_{}".format(os)]
          fm_h, fm_w = x.shape[2:]
          loc, pred = ssd_head(x)
  
          locations.append(loc)
          confidences.append(pred)
  
          anchors_fm_ctr = self.anchor_box_generator(
              fm_height=fm_h,
              fm_width=fm_w,
              fm_output_stride=os,
              device=device,
          )
          anchors.append(anchors_fm_ctr.to(device=device))
  
      locations = torch.cat(locations, dim=1)
      confidences = torch.cat(confidences, dim=1)
  
      anchors = torch.cat(anchors, dim=0)
      anchors = anchors.unsqueeze(dim=0)
  
      return confidences, locations, anchors
  
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 6 for calling the SingleShotMaskDetector obj's underlying member method code with example usage: obj.ssd_forward(end_points, device) and returns: confidences,  locations,  anchors