class_docstr: Residual convolution module.
class_docstr_tok: ["Residual","convolution","module","."]
docstr: Forward pass.
  
  Args:
      x (tensor): input
  
  Returns:
      tensor: output
docstr_tok: ["Forward","pass",".","Args",":","x","(","tensor",")",":","input","Returns",":","tensor",":","output"]
code: def forward(self, x):
      """Forward pass.
  
      Args:
          x (tensor): input
  
      Returns:
          tensor: output
      """
  
      out = self.activation(x)
      out = self.conv1(out)
      if self.bn == True:
          out = self.bn1(out)
  
      out = self.activation(out)
      out = self.conv2(out)
      if self.bn == True:
          out = self.bn2(out)
  
      if self.groups > 1:
          out = self.conv_merge(out)
  
      return self.skip_add.add(out, x)
  
imports: import torch
  import torch.nn
  from .vit import _make_pretrained_vitb16_384, _make_pretrained_vitb_rn50_384, _make_pretrained_vitl16_384
label_desc: Use image node_id 2 for calling the ResidualConvUnit_custom obj's underlying member method code with example usage: obj.forward(x) and returns: self