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 forward(
      self, x: Union[Tensor, Dict]
  ) -> Union[Tuple[Tensor, ...], Tuple[Any, ...], Dict]:
      if isinstance(x, Dict):
          input_tensor = x["image"]
      elif isinstance(x, Tensor):
          input_tensor = x
      else:
          raise NotImplementedError(
              "Input to SSD should be either a Tensor or a Dict of Tensors"
          )
  
      device = input_tensor.device
      backbone_end_points: Dict = self.get_backbone_features(
          input_tensor
      )
  
      if not is_coreml_conversion(self.opts):
          confidences, locations, anchors = self.ssd_forward(
              end_points=backbone_end_points, device=device
          )
  
          output_dict = {"scores": confidences, "boxes": locations}
  
          if not self.training:
              # compute the detection results during evaluation
              scores = nn.Softmax(dim=-1)(confidences)
              boxes = self.match_prior.convert_to_boxes(
                  pred_locations=locations, anchors=anchors
              )
  
              detections = self.postprocess_detections(
                  boxes=boxes, scores=scores
              )
              output_dict["detections"] = detections
  
          return output_dict
      else:
          return self.ssd_forward(
              end_points=backbone_end_points, is_prediction=False
          )
  
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 7 for calling the SingleShotMaskDetector obj's underlying member method code with example usage: obj.forward(x) and returns: output_dict, self