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: 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"""
      bsz, channels, width, height = x.shape
      if bsz != 1:
          logger.error(
              "Prediction is supported with a batch size of 1 in {}".format(
                  self.__class__.__name__
              )
          )
  
      device = x.device
      enc_end_points: Dict = self.get_backbone_features(x)
      confidences, locations, anchors = self.ssd_forward(
          end_points=enc_end_points, device=device
      )
  
      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
      )[0]
      return detections
  
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 8 for calling the SingleShotMaskDetector obj's underlying member method code with example usage: obj.predict(x) and returns: detections