class_docstr: Detection block in YOLO neck.
  
  Let out_channels = n, the DetectionBlock contains:
  Six ConvLayers, 1 Conv2D Layer and 1 YoloLayer.
  The first 6 ConvLayers are formed the following way:
      1x1xn, 3x3x2n, 1x1xn, 3x3x2n, 1x1xn, 3x3x2n.
  The Conv2D layer is 1x1x255.
  Some block will have branch after the fifth ConvLayer.
  The input channel is arbitrary (in_channels)
  
  Args:
      in_channels (int): The number of input channels.
      out_channels (int): The number of output channels.
      conv_cfg (dict): Config dict for convolution layer. Default: None.
      norm_cfg (dict): Dictionary to construct and config norm layer.
          Default: dict(type='BN', requires_grad=True)
      act_cfg (dict): Config dict for activation layer.
          Default: dict(type='LeakyReLU', negative_slope=0.1).
code: def forward(self, x):
      tmp = self.conv1(x)
      tmp = self.conv2(tmp)
      tmp = self.conv3(tmp)
      tmp = self.conv4(tmp)
      out = self.conv5(tmp)
      return out
  
file_path: insightface/detection/scrfd/mmdet/models/necks/yolo_neck.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule
  from ..builder import NECKS
label_desc: Use image node_id 2 for calling the DetectionBlock obj's underlying member method code with example usage: obj.forward(x) and returns: out