class_docstr: Early Fusion Module.
  
  Args:
      v_dim (int): Dimension of visual features.
      l_dim (int): Dimension of language features.
      embed_dim (int): The embedding dimension for the attention operation.
      num_heads (int): Number of attention heads.
      dropout (float): Dropout probability.
      drop_path (float): Drop path probability.
      use_checkpoint (bool): Whether to use PyTorch's checkpoint function.
class_docstr_tok: ["Early","Fusion","Module",".","Args",":","v_dim","(","int",")",":","Dimension","of","visual","features",".","l_dim","(","int",")",":","Dimension","of","language","features",".","embed_dim","(","int",")",":","The","embedding","dimension","for","the","attention","operation",".","num_heads","(","int",")",":","Number","of","attention","heads",".","dropout","(","float",")",":","Dropout","probability",".","drop_path","(","float",")",":","Drop","path","probability",".","use_checkpoint","(","bool",")",":","Whether","to","use","PyTorch","'s","checkpoint","function","."]
docstr: Forward pass of the VLFuse module.
docstr_tok: ["Forward","pass","of","the","VLFuse","module","."]
code: def forward(self, x: dict) -> dict:
      """Forward pass of the VLFuse module."""
      visual_features = x["visual"]
      language_dict_features = x["lang"]
  
      if self.use_checkpoint:
          # vf is mean visual_features
          # checkpoint does not allow complex data structures as input,
          # such as list, so we must split them.
          (
              vf0,
              vf1,
              vf2,
              vf3,
              vf4,
              language_features,
          ) = checkpoint.checkpoint(
              self.b_attn,
              *visual_features,
              language_dict_features["hidden"],
              language_dict_features["masks"],
          )
      else:
          vf0, vf1, vf2, vf3, vf4, language_features = self.b_attn(
              *visual_features,
              language_dict_features["hidden"],
              language_dict_features["masks"],
          )
  
      language_dict_features["hidden"] = language_features
      fused_language_dict_features = language_dict_features
  
      features_dict = {
          "visual": [vf0, vf1, vf2, vf3, vf4],
          "lang": fused_language_dict_features,
      }
  
      return features_dict
  
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 VLFuse obj's underlying member method code with example usage: obj.forward(x) and returns: features_dict