class_docstr: This is an implementation of `SSH context module` described in `SSH:
  Single Stage Headless Face Detector.
  
  <https://arxiv.org/pdf/1708.03979.pdf>`_.
  
  Args:
      in_channels (int): Number of input channels used at each scale.
      out_channels (int): Number of output channels used at each scale.
      conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
          convolution layer. Defaults to None.
      norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization
          layer. Defaults to dict(type='BN').
      init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or
          list[dict], optional): Initialization config dict.
          Defaults to None.
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code: def forward(self, x: torch.Tensor) -> tuple:
      conv5x5_1 = self.conv5x5_1(x)
      conv5x5 = self.conv5x5_2(conv5x5_1)
      conv7x7_2 = self.conv7x7_2(conv5x5_1)
      conv7x7 = self.conv7x7_3(conv7x7_2)
  
      return (conv5x5, conv7x7)
  
file_path: mmdetection/mmdet/models/necks/ssh.py
imports: from typing import List, Tuple
  import torch
  import torch.nn.functional
  from mmcv.cnn import ConvModule
  from mmengine.model import BaseModule
  from mmdet.registry import MODELS
  from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
label_desc: Use image node_id 2 for calling the SSHContextModule obj's underlying member method code with example usage: obj.forward(x) and returns: conv5x5,  conv7x7