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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docstr: Perform a single attention call between the visual and language
  inputs.
  
  Args:
  visual (Tensor): The visual input tensor.
  lang (Tensor): The language input tensor.
  attention_mask_v (Optional[Tensor]):
      An optional attention mask tensor for the visual input.
  attention_mask_l (Optional[Tensor]):
      An optional attention mask tensor for the language input.
  
  Returns:
      Tuple[Tensor, Tensor]: A tuple containing the updated
          visual and language tensors after the attention call.
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code: def single_attention_call(
      self,
      visual: Tensor,
      lang: Tensor,
      attention_mask_v: Optional[Tensor] = None,
      attention_mask_l: Optional[Tensor] = None,
  ) -> Tuple[Tensor, Tensor]:
      """Perform a single attention call between the visual and language
      inputs.
  
      Args:
      visual (Tensor): The visual input tensor.
      lang (Tensor): The language input tensor.
      attention_mask_v (Optional[Tensor]):
          An optional attention mask tensor for the visual input.
      attention_mask_l (Optional[Tensor]):
          An optional attention mask tensor for the language input.
  
      Returns:
          Tuple[Tensor, Tensor]: A tuple containing the updated
              visual and language tensors after the attention call.
      """
      visual = self.layer_norm_v(visual)
      lang = self.layer_norm_l(lang)
      delta_v, delta_l = self.attn(
          visual,
          lang,
          attention_mask_v=attention_mask_v,
          attention_mask_l=attention_mask_l,
      )
      # visual, lang = visual + delta_v, l + delta_l
      visual = visual + self.drop_path(self.gamma_v * delta_v)
      lang = lang + self.drop_path(self.gamma_l * delta_l)
      return visual, lang
  
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 3 for calling the BiAttentionBlock obj's underlying member method code with example usage: obj.single_attention_call(visual, lang, attention_mask_v, attention_mask_l) and returns: visual,  lang