fixes for B007

        x_t = None

        logs = None

        for n in range(n_runs):

        for _ in range(n_runs):

            if custom_shape is not None:

                x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)

                x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])

    added = []

    for k, v in params.items():

    for k in params:

        if not k.startswith("AddNet Model "):

            continue

        model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)

        model.load_state_dict(torch.load(filename), strict=True)

        model.eval()

        for k, v in model.named_parameters():

        for _, v in model.named_parameters():

            v.requires_grad = False

        model = model.to(device)

        H, W = self.patches_resolution

        flops += H * W * 3 * self.embed_dim * 9

        flops += self.patch_embed.flops()

        for i, layer in enumerate(self.layers):

        for layer in self.layers:

            flops += layer.flops()

        flops += H * W * 3 * self.embed_dim * self.embed_dim

        flops += self.upsample.flops()

        H, W = self.patches_resolution

        flops += H * W * 3 * self.embed_dim * 9

        flops += self.patch_embed.flops()

        for i, layer in enumerate(self.layers):

        for layer in self.layers:

            flops += layer.flops()

        flops += H * W * 3 * self.embed_dim * self.embed_dim

        flops += self.upsample.flops()