code: def forward(self):
      input = torch.tensor([[1, 2, 4, 5], [4, 3, 2, 9]])
      input2 = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9])
      embedding_matrix = torch.rand(10, 3)
      offsets = torch.tensor([0, 4])
      return len(
          F.embedding(input, embedding_matrix),
          F.embedding_bag(input2, embedding_matrix, offsets),
          F.one_hot(torch.arange(0, 5) % 3, num_classes=5),
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 for calling the NNSparseModule obj's underlying member method code with example usage: obj.forward() and returns: len