class_docstr: This class defines the `SSD object detection Head <https://arxiv.org/abs/1512.02325>`_
  
  Args:
      opts: command-line arguments
      in_channels (int): :math:`C` from an expected input of size :math:`(N, C, H, W)`
      n_anchors (int): Number of anchors
      n_classes (int): Number of classes in the dataset
      n_coordinates (Optional[int]): Number of coordinates. Default: 4 (x, y, w, h)
      proj_channels (Optional[int]): Number of projected channels. If `-1`, then projection layer is not used
      kernel_size (Optional[int]): Kernel size in convolutional layer. If kernel_size=1, then standard
          point-wise convolution is used. Otherwise, separable convolution is used
      stride (Optional[int]): stride for feature map. If stride > 1, then feature map is sampled at this rate
          and predictions are made on fewer pixels as compared to the input tensor. Default: 1
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code: def forward(
      self, x: Tensor, *args, **kwargs
  ) -> Tuple[Tensor, Tensor]:
      batch_size = x.shape[0]
  
      if self.proj_layer is not None:
          x = self.proj_layer(x)
  
      # [B x C x H x W] --> [B x Anchors * (coordinates + classes) x H x W]
      x = self.loc_cls_layer(x)
  
      if self.stride > 1:
          x = self._sample_fm(x)
  
      # [B x Anchors * (coordinates + classes) x H x W] --> [B x H x W x Anchors * (coordinates + classes)]
      x = x.permute(0, 2, 3, 1)
      # [B x H x W x Anchors * (coordinates + classes)] --> [B x H*W*Anchors X (coordinates + classes)]
      x = x.contiguous().view(
          batch_size, -1, self.n_coordinates + self.n_classes
      )
  
      # [B x H*W*Anchors X (coordinates + classes)] --> [B x H*W*Anchors X coordinates], [B x H*W*Anchors X classes]
      box_locations, box_classes = torch.split(
          x, [self.n_coordinates, self.n_classes], dim=-1
      )
      return box_locations, box_classes
  
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor, nn
  from torchvision.ops.roi_align import RoIAlign
  from cvnets.layers import ConvLayer2d, SeparableConv2d, TransposeConvLayer2d
  from cvnets.misc.init_utils import initialize_conv_layer
  from cvnets.modules import BaseModule
label_desc: Use image node_id 5 for calling the SSDHead obj's underlying member method code with example usage: obj.forward(x) and returns: box_locations,  box_classes