class_docstr: This is a PyTorch implementation of the paper Disentangled Representation Learning for Text-Video Retrieval.
  Args:
      base_encoder (`str`):
          CLIP encoder backbone. default: `clip_vit_b32`
      agg_module (`str`):
          Feature aggregation module for video. default: `seqTransf`, choices=[`ndone`, `seqLSTM`, `seqTransf`]
      interaction (`str`):
          Interaction type for retrieval. default: `wti`.
      wti_arch (`int)`:
          Select a architecture for weight branch. default: 2.
      cdcr (`int`):
          Channel decorrelation regularization. default: 3.
      cdcr_alpha1 (`float`):
          Coefficient 1 for channel decorrelation regularization. default: 1.0.
      cdcr_alpha2 (`float`):
          Coefficient 2 for channel decorrelation regularization. default: 0.06.
      cdcr_lambda (`float`):
          Coefficient for channel decorrelation regularization. default: 0.001.
      cross_num_hidden_layers (`int`):
          Number of hidden layers for cross transformer interaction.
class_docstr_tok: ["This","is","a","PyTorch","implementation","of","the","paper","Disentangled","Representation","Learning","for","Text-Video","Retrieval",".","Args",":","base_encoder","(","`","str","`",")",":","CLIP","encoder","backbone",".","default",":","`","clip_vit_b32","`","agg_module","(","`","str","`",")",":","Feature","aggregation","module","for","video",".","default",":","`","seqTransf","`",",","choices=","[","`","ndone","`",",","`","seqLSTM","`",",","`","seqTransf","`","]","interaction","(","`","str","`",")",":","Interaction","type","for","retrieval",".","default",":","`","wti","`",".","wti_arch","(","`","int",")","`",":","Select","a","architecture","for","weight","branch",".","default",":","2.","cdcr","(","`","int","`",")",":","Channel","decorrelation","regularization",".","default",":","3.","cdcr_alpha1","(","`","float","`",")",":","Coefficient","1","for","channel","decorrelation","regularization",".","default",":","1.0.","cdcr_alpha2","(","`","float","`",")",":","Coefficient","2","for","channel","decorrelation","regularization",".","default",":","0.06.","cdcr_lambda","(","`","float","`",")",":","Coefficient","for","channel","decorrelation","regularization",".","default",":","0.001.","cross_num_hidden_layers","(","`","int","`",")",":","Number","of","hidden","layers","for","cross","transformer","interaction","."]
code: def xti_interaction(
      self, text_feat, video_feat, text_mask, video_mask
  ):
      text_feat = self.get_text_sep_feat(
          text_feat, text_mask
      )  # bd x 1 x dd
  
      b_text, s_text, d_text = text_feat.size()
      b_video, s_video, d_video = video_feat.size()
      if (
          self.training and torch.cuda.is_available()
      ):  # batch merge here
          text_feat_full = allgather2(text_feat, self.config)
          video_feat_full = allgather2(video_feat, self.config)
          video_mask_full = allgather2(video_mask, self.config)
          text_feat = text_feat_full[
              b_text
              * self.config.local_rank : b_text
              * (1 + self.config.local_rank)
          ]
          video_feat = video_feat_full[
              b_video
              * self.config.local_rank : b_video
              * (1 + self.config.local_rank)
          ]
          torch.distributed.barrier()  # force sync
      else:
          text_feat_full = text_feat
          video_feat_full = video_feat
          video_mask_full = video_mask
  
      b_text_full = text_feat_full.shape[0]
      b_video_full = video_feat_full.shape[0]
  
      text_mask = torch.ones(text_feat.size(0), 1).to(
          device=text_mask.device, dtype=text_mask.dtype
      )
      text_mask_full = torch.ones(text_feat_full.size(0), 1).to(
          device=text_mask.device, dtype=text_mask.dtype
      )
  
      # tV
      text_feat_1 = text_feat.unsqueeze(1).repeat(
          1, b_video_full, 1, 1
      )  # b_t x bd_v x n_t x d_t
      text_feat_1 = text_feat_1.view(
          -1, s_text, d_text
      )  # (b_t x bd_v) x n_t x d_t
      text_mask_1 = text_mask.unsqueeze(1).repeat(
          1, b_video_full, 1
      )  # b_t x bd_v x 1
      text_mask_1 = text_mask_1.view(-1, s_text)  # (b_t x bd_v) x 1
  
      video_feat_1 = video_feat_full.unsqueeze(0).repeat(
          b_text, 1, 1, 1
      )  # b_t x bd_v x n_v x d_t
      video_feat_1 = video_feat_1.view(
          -1, s_video, d_video
      )  # (b_t x bd_v) x n_v x d_v
      video_mask_1 = video_mask_full.unsqueeze(0).repeat(
          b_text, 1, 1
      )  # b_t x bd_v x n_v
      video_mask_1 = video_mask_1.view(
          -1, s_video
      )  # (b_t x bd_v) x n_v
  
      # vT
      text_feat_2 = text_feat_full.unsqueeze(1).repeat(
          1, b_video, 1, 1
      )  # bd_t x b_v x n_t x d_t
      text_feat_2 = text_feat_2.view(
          -1, s_text, d_text
      )  # (bd_t x b_v) x n_t x d_t
      text_mask_2 = text_mask_full.unsqueeze(1).repeat(
          1, b_video, 1
      )  # bd_t x b_v x 1
      text_mask_2 = text_mask_2.view(-1, s_text)  # (bd_t x b_v) x 1
  
      video_feat_2 = video_feat.unsqueeze(0).repeat(
          b_text_full, 1, 1, 1
      )  # bd_t x b_v x n_v x d_v
      video_feat_2 = video_feat_2.view(
          -1, s_video, d_video
      )  # (bd_t x b_v) x n_v x d_t
      video_mask_2 = video_mask.unsqueeze(0).repeat(
          b_text_full, 1, 1
      )  # bd_t x b_v x n_v
      video_mask_2 = video_mask_2.view(
          -1, s_video
      )  # (bd_t x b_v) x n_v
  
      _, pooled_feat, _ = self._get_cross_feat(
          text_feat_1, video_feat_1, text_mask_1, video_mask_1
      )
      retrieve_logits_tv = (
          self.similarity_dense(pooled_feat)
          .squeeze(-1)
          .view(b_text, b_video_full)
      )
      _, pooled_feat, _ = self._get_cross_feat(
          text_feat_2, video_feat_2, text_mask_2, video_mask_2
      )
      retrieve_logits_vt = (
          self.similarity_dense(pooled_feat)
          .squeeze(-1)
          .view(b_text_full, b_video)
          .T
      )
  
      if self.training:
          logit_scale = self.clip.logit_scale.exp()  #
          retrieve_logits_tv = torch.roll(
              retrieve_logits_tv, -b_text * self.config.local_rank, -1
          )
          retrieve_logits_vt = torch.roll(
              retrieve_logits_vt, -b_video * self.config.local_rank, -1
          )
          retrieve_logits_tv = logit_scale * retrieve_logits_tv
          retrieve_logits_vt = logit_scale * retrieve_logits_vt
  
          return retrieve_logits_tv, retrieve_logits_vt, 0.0
      else:
          return retrieve_logits_tv, retrieve_logits_vt, 0.0
  
imports: from collections import OrderedDict
  from typing import Optional
  from types import SimpleNamespace
  import torch
  import logging
  from torch import nn
  from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
  from towhee.models import clip
  from towhee.models.drl.until_module import convert_weights
  from towhee.models.drl.module_cross import CrossModel, Transformer
  from towhee.models.drl.until_module import LayerNorm, AllGather, AllGather2, CrossEn
label_desc: Use image node_id 11 for calling the DRL obj's underlying member method code with example usage: obj.xti_interaction(text_feat, video_feat, text_mask, video_mask) and returns: retrieve_logits_tv,  retrieve_logits_vt,  int, retrieve_logits_tv,  retrieve_logits_vt,  int