class_docstr: BiAttentionBlock Module:
  
  First, multi-level visual features are concat; Then the concat visual
  feature and lang feature are fused by attention; Finally the newly visual
  feature are split into multi levels.
  
  Args:
      v_dim (int): The dimension of the visual features.
      l_dim (int): The dimension of the language feature.
      embed_dim (int): The embedding dimension for the attention operation.
      num_heads (int): The number of attention heads.
      dropout (float, optional): The dropout probability. Defaults to 0.1.
      drop_path (float, optional): The drop path probability.
          Defaults to 0.0.
      init_values (float, optional):
          The initial value for the scaling parameter.
          Defaults to 1e-4.
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code: def forward(
      self,
      vf0: Tensor,
      vf1: Tensor,
      vf2: Tensor,
      vf3: Tensor,
      vf4: Tensor,
      lang_feature: Tensor,
      attention_mask_l=None,
  ):
      visual_features = [vf0, vf1, vf2, vf3, vf4]
      size_per_level, visual_features_flatten = [], []
      for i, feat_per_level in enumerate(visual_features):
          bs, c, h, w = feat_per_level.shape
          size_per_level.append([h, w])
          feat = permute_and_flatten(feat_per_level, bs, -1, c, h, w)
          visual_features_flatten.append(feat)
      visual_features_flatten = torch.cat(
          visual_features_flatten, dim=1
      )
      new_v, new_lang_feature = self.single_attention_call(
          visual_features_flatten,
          lang_feature,
          attention_mask_l=attention_mask_l,
      )
      # [bs, N, C] -> [bs, C, N]
      new_v = new_v.transpose(1, 2).contiguous()
  
      start = 0
      # fvfs is mean fusion_visual_features
      fvfs = []
      for h, w in size_per_level:
          new_v_per_level = (
              new_v[:, :, start : start + h * w]
              .view(bs, -1, h, w)
              .contiguous()
          )
          fvfs.append(new_v_per_level)
          start += h * w
  
      return (
          fvfs[0],
          fvfs[1],
          fvfs[2],
          fvfs[3],
          fvfs[4],
          new_lang_feature,
      )
  
imports: import math
  from typing import Dict, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from mmcv.cnn.bricks import DropPath
  from torch import Tensor
label_desc: Use image node_id 2 for calling the BiAttentionBlock obj's underlying member method code with example usage: obj.forward(vf0, vf1, vf2, vf3, vf4, lang_feature, attention_mask_l) and returns: fvfs,  fvfs,  fvfs,  fvfs,  fvfs,  new_lang_feature