Unet(
  (model): UnetBlock(
    (model): Sequential(
      (0): Conv2d(1, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (1): UnetBlock(
        (model): Sequential(
          (0): LeakyReLU(negative_slope=0.2, inplace=True)
          (1): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
          (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (3): UnetBlock(
            (model): Sequential(
              (0): LeakyReLU(negative_slope=0.2, inplace=True)
              (1): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
              (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (3): UnetBlock(
                (model): Sequential(
                  (0): LeakyReLU(negative_slope=0.2, inplace=True)
                  (1): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
                  (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                  (3): UnetBlock(
                    (model): Sequential(
                      (0): LeakyReLU(negative_slope=0.2, inplace=True)
                      (1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
                      (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                      (3): UnetBlock(
                        (model): Sequential(
                          (0): LeakyReLU(negative_slope=0.2, inplace=True)
                          (1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
                          (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                          (3): UnetBlock(
                            (model): Sequential(
                              (0): LeakyReLU(negative_slope=0.2, inplace=True)
                              (1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
                              (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (3): UnetBlock(
                                (model): Sequential(
                                  (0): LeakyReLU(negative_slope=0.2, inplace=True)
                                  (1): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
                                  (2): ReLU(inplace=True)
                                  (3): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
                                  (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                                )
                              )
                              (4): ReLU(inplace=True)
                              (5): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
                              (6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (7): Dropout(p=0.5, inplace=False)
                            )
                          )
                          (4): ReLU(inplace=True)
                          (5): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
                          (6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                          (7): Dropout(p=0.5, inplace=False)
                        )
                      )
                      (4): ReLU(inplace=True)
                      (5): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
                      (6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                      (7): Dropout(p=0.5, inplace=False)
                    )
                  )
                  (4): ReLU(inplace=True)
                  (5): ConvTranspose2d(1024, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
                  (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                )
              )
              (4): ReLU(inplace=True)
              (5): ConvTranspose2d(512, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
              (6): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (4): ReLU(inplace=True)
          (5): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
          (6): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (2): ReLU(inplace=True)
      (3): ConvTranspose2d(128, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
      (4): Tanh()
    )
  )
)

ternitik i authenticate