class_docstr: This is a sequential container which calls the Conv2d modules with extra Add.
  During quantization this will be replaced with the corresponding fused module.
class_docstr_tok: ["This","is","a","sequential","container","which","calls","the","Conv2d","modules","with","extra","Add",".","During","quantization","this","will","be","replaced","with","the","corresponding","fused","module","."]
code: def forward(self, x1, x2):
      return self.add(self[0](x1), x2)
  
imports: import torch
  from torch.nn import Conv1d, Conv2d, Conv3d, ReLU, Linear, BatchNorm1d, BatchNorm2d, BatchNorm3d
  from torch.nn.utils.parametrize import type_before_parametrizations
label_desc: Use image node_id 2 for calling the ConvAdd2d obj's underlying member method code with example usage: obj.forward(x1, x2) and returns: self