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.
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code: def dp_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
  
      if (
          self.training and torch.cuda.is_available()
      ):  # batch merge here
          text_feat = allgather(text_feat, self.config)
          video_feat = allgather(video_feat, self.config)
          video_mask = allgather(video_mask, self.config)
          torch.distributed.barrier()  # force sync
  
      text_feat = text_feat.squeeze(1)  # bd x 1 x dd -> bd x dd
      text_feat = text_feat / text_feat.norm(
          dim=-1, keepdim=True
      )  # bd x dd
  
      video_feat = video_feat / video_feat.norm(dim=-1, keepdim=True)
      video_feat = self.get_video_avg_feat(
          video_feat, video_mask
      )  # bd x nd_v x dd -> bd x dd
      video_feat = video_feat / video_feat.norm(dim=-1, keepdim=True)
  
      retrieve_logits = torch.matmul(text_feat, video_feat.t())
      if self.training:
          logit_scale = self.clip.logit_scale.exp()  #
          retrieve_logits = logit_scale * retrieve_logits
  
          if self.config.cdcr != 0:
              z_a_norm = (
                  text_feat - text_feat.mean(0)
              ) / text_feat.std(
                  0
              )  # bdxdd
              z_b_norm = (
                  video_feat - video_feat.mean(0)
              ) / video_feat.std(
                  0
              )  # bdxdd
  
              # cross-correlation matrix
              bd, dd = z_a_norm.shape
              c = (
                  torch.einsum("bm,bn->mn", z_a_norm, z_b_norm) / bd
              )  # ddxdd
              # loss
              on_diag = torch.diagonal(c).add_(-1).pow_(2).sum()
              off_diag = (
                  c.flatten()[1:]
                  .view(dd - 1, dd + 1)[:, :-1]
                  .pow_(2)
                  .sum()
              )
              cdcr_loss = (
                  on_diag * self.config.cdcr_alpha1
                  + off_diag * self.config.cdcr_alpha2
              )
  
              return retrieve_logits, retrieve_logits.T, cdcr_loss
          else:
              return retrieve_logits, retrieve_logits.T, 0.0
      else:
          return retrieve_logits, retrieve_logits.T, 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 9 for calling the DRL obj's underlying member method code with example usage: obj.dp_interaction(text_feat, video_feat, text_mask, video_mask) and returns: retrieve_logits,  retrieve_logits,  int, retrieve_logits,  retrieve_logits,  cdcr_loss, retrieve_logits,  retrieve_logits,  int