class_docstr: This is a sequential container which calls the Linear and LeakyReLU modules.
  During quantization this will be replaced with the corresponding fused module.
class_docstr_tok: ["This","is","a","sequential","container","which","calls","the","Linear","and","LeakyReLU","modules",".","During","quantization","this","will","be","replaced","with","the","corresponding","fused","module","."]
code: def __init__(self, linear, leaky_relu):
      assert (
          type(linear) == Linear
          and type(leaky_relu) == torch.nn.LeakyReLU
      ), f"Incorrect types for input modules{type(linear)}{type(leaky_relu)}"
      super().__init__(linear, leaky_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 LinearLeakyReLU object from inherited base classes: _FusedModule with example: obj = LinearLeakyReLU(linear, leaky_relu)