class_docstr: This is a sequential container which calls the Conv2d and ReLU modules.
  During quantization this will be replaced with the corresponding fused module.
class_docstr_tok: ["This","is","a","sequential","container","which","calls","the","Conv2d","and","ReLU","modules",".","During","quantization","this","will","be","replaced","with","the","corresponding","fused","module","."]
code: def __init__(self, conv, relu):
      assert (
          type_before_parametrizations(conv) == Conv2d
          and type_before_parametrizations(relu) == ReLU
      ), f"Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(relu)}"
      super().__init__(conv, 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 ConvReLU2d object from inherited base classes: _FusedModule with example: obj = ConvReLU2d(conv, relu)