code: def forward(
      self,
      video_hidden_states=None,
      target_video_hidden_states=None,
      non_masked_frame_hidden_states=None,
      text_hidden_states=None,
  ):
      video_logits, text_logits = None, None
      if video_hidden_states is not None:
          video_hidden_states = self.transform(video_hidden_states)
          non_masked_frame_logits = torch.mm(
              video_hidden_states,
              non_masked_frame_hidden_states.transpose(1, 0),
          )
          masked_frame_logits = torch.bmm(
              video_hidden_states.unsqueeze(1),
              target_video_hidden_states.unsqueeze(-1),
          ).squeeze(-1)
          video_logits = torch.cat(
              [masked_frame_logits, non_masked_frame_logits], dim=1
          )
  
      if text_hidden_states is not None:
          text_hidden_states = self.transform(text_hidden_states)
          text_logits = self.decoder(text_hidden_states)
      return video_logits, text_logits
  
imports: import torch
  from torch import nn
  from ..modules import VideoTokenMLP, MMBertEmbeddings
label_desc: Use image node_id 2 for calling the BertMFMMLMPredictionHead obj's underlying member method code with example usage: obj.forward(video_hidden_states, target_video_hidden_states, non_masked_frame_hidden_states, text_hidden_states) and returns: video_logits,  text_logits