def forward(self, x):  # noqa: F811
        if isinstance(x, torch.Tensor):
            prev_features = [x]
        else:
            prev_features = x

        if self.memory_efficient and self.any_requires_grad(prev_features):
            if torch.jit.is_scripting():
                raise Exception("Memory Efficient not supported in JIT")
            bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
        else:
            bottleneck_output = self.bottleneck_fn(prev_features)

        new_features = self.conv2(self.norm2(bottleneck_output))
        if self.drop_rate > 0:
            new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
        return new_features