class_docstr: This is a sequential container which calls the Conv2d, add, Relu.
  During quantization this will be replaced with the corresponding fused module.
class_docstr_tok: ["This","is","a","sequential","container","which","calls","the","Conv2d",",","add",",","Relu",".","During","quantization","this","will","be","replaced","with","the","corresponding","fused","module","."]
code: def __init__(self, conv, add, relu):
      super().__init__(conv)
      self.add = add
      self.relu = relu
  
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 1 to create a new ConvAddReLU2d object from inherited base classes: _FusedModule with example: obj = ConvAddReLU2d(conv, add, relu)