'NoneType' object is not callable

Hello, thank you for your interesting work. I want to use a pretrained model of yours but i face an error. I use videomamba.py file for loading the model in my model. I removed the classification head in videomamba.py:

```
 .
 .
 .
def forward_features(self, x, inference_params=None):

    x = self.patch_embed(x)
    B, C, T, H, W = x.shape
    x = x.permute(0, 2, 3, 4, 1).reshape(B * T, H * W, C)

    cls_token = self.cls_token.expand(x.shape[0], -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
    x = torch.cat((cls_token, x), dim=1)
    x = x + self.pos_embed

    # temporal pos
    cls_tokens = x[:B, :1, :]
    x = x[:, 1:]
    x = rearrange(x, '(b t) n m -> (b n) t m', b=B, t=T)
    x = x + self.temporal_pos_embedding
    x = rearrange(x, '(b n) t m -> b (t n) m', b=B, t=T)
    x = torch.cat((cls_tokens, x), dim=1)

    x = self.pos_drop(x)

    # mamba impl
    residual = None
    hidden_states = x
    for idx, layer in enumerate(self.layers):
        if self.use_checkpoint and idx < self.checkpoint_num:
            hidden_states, residual = layer(
                hidden_states, residual, inference_params=inference_params,
                use_checkpoint=True
            )
        else:
            hidden_states, residual = layer(
                hidden_states, residual, inference_params=inference_params
            )

    if not self.fused_add_norm:
        if residual is None:
            residual = hidden_states
        else:
            residual = residual + self.drop_path(hidden_states)
        hidden_states = self.norm_f(residual.to(dtype=self.norm_f.weight.dtype))
    else:
        # Set prenorm=False here since we don't need the residual
        fused_add_norm_fn = rms_norm_fn if isinstance(self.norm_f, RMSNorm) else layer_norm_fn
        hidden_states = fused_add_norm_fn(
            self.drop_path(hidden_states),
            self.norm_f.weight,
            self.norm_f.bias,
            eps=self.norm_f.eps,
            residual=residual,
            prenorm=False,
            residual_in_fp32=self.residual_in_fp32,
        )

    # return only cls token
    return hidden_states[:, 0, :]

def forward(self, x, inference_params=None):

    x1= self.forward_features(x, inference_params)

    return x1
 ```

What is wrong?

OpenGVLab / VideoMamba

'NoneType' object is not callable #17