code: def forward(self, x: Tensor) -> Tensor:
      xs = [emb(b) for emb, b in zip(self.embs, x.t())]
      return torch.stack(xs, dim=0).sum(0)
  
imports: import argparse
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch_geometric.data import Data, InMemoryDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import LCMAggregation
  from torch_geometric.transforms import BaseTransform
label_desc: Use image node_id 2 for calling the BitwiseEmbedding obj's underlying member method code with example usage: obj.forward(x) and returns: torch