class_docstr: fine-tuning model for retrival task.
code: def forward(
      self, caps, cmasks, vfeats, vmasks, attention_mask=None, **kwargs
  ):
      # ActionLocalization assume of batch_size=1, squeeze it.
      caps = caps.squeeze(0)
      cmasks = cmasks.squeeze(0)
      vfeats = vfeats.squeeze(0)
      vmasks = vmasks.squeeze(0)
      attention_mask = (
          attention_mask.squeeze(0)
          if attention_mask is not None
          else None
      )
  
      # TODO (huxu): other ways to do negative examples; move the following
      # into your criterion forward.
      output_hidden_states = True
  
      # a len1 dummy video token.
      dummy_vfeats = torch.zeros(
          (caps.size(0), 1, vfeats.size(-1)),
          device=vfeats.device,
          dtype=vfeats.dtype,
      )
      dummy_vmasks = torch.ones(
          (caps.size(0), 1), dtype=torch.bool, device=vfeats.device
      )
  
      dummy_caps = (
          torch.LongTensor(
              [
                  [
                      self.cls_token_id,
                      self.sep_token_id,
                      self.pad_token_id,
                      self.sep_token_id,
                  ]
              ],
          )
          .to(caps.device)
          .repeat(vfeats.size(0), 1)
      )
      dummy_cmasks = (
          torch.BoolTensor(
              [[0, 1, 0, 1]]  # pad are valid for attention.
          )
          .to(caps.device)
          .repeat(vfeats.size(0), 1)
      )
  
      #  video forwarding, text is dummy; never use attention_mask.
      attention_mask, token_type_ids = self._mm_on_the_fly(
          dummy_cmasks, vmasks, None
      )
  
      outputs = self.mm_encoder(
          input_ids=dummy_caps,
          input_video_embeds=vfeats,
          attention_mask=attention_mask,
          token_type_ids=token_type_ids,
          output_hidden_states=output_hidden_states,
      )
  
      layer_idx = (
          self.last_iso_layer
          if self.last_iso_layer > 0
          else self.num_hidden_layers
      )
  
      video_seq = (
          outputs[2][layer_idx][:, 1 : vmasks.size(1) + 1]
          .masked_select(vmasks.unsqueeze(-1))
          .view(-1, self.hidden_size)
      )
  
      # text forwarding, video is dummy
      attention_mask, token_type_ids = self._mm_on_the_fly(
          cmasks, dummy_vmasks, None
      )
  
      outputs = self.mm_encoder(
          input_ids=caps,
          input_video_embeds=dummy_vfeats,
          attention_mask=attention_mask,
          token_type_ids=token_type_ids,
          output_hidden_states=output_hidden_states,
      )
  
      _, pooled_text = self._pooling_vt_layer(
          outputs[2], cmasks, dummy_vmasks
      )
      # this line is not right.
      logits = torch.mm(video_seq, pooled_text.transpose(1, 0))
      return {"logits": logits}
  
imports: import torch
  from torch import nn
  from .None import transformermodel
label_desc: Use image node_id 2 for calling the MMFusionActionLocalization obj's underlying member method code with example usage: obj.forward(caps, cmasks, vfeats, vmasks, attention_mask) and returns: dict