src/transformers/models/convnext/modeling_convnext.py/ConvNextLayer/forward
class ConvNextLayer:    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
        input = hidden_states
        x = self.dwconv(hidden_states)
        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
        x = self.layernorm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.pwconv2(x)
        if self.layer_scale_parameter is not None:
            x = self.layer_scale_parameter * x
        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)

        x = input + self.drop_path(x)
        return x