code: def test_dense(val_shape):
      ctx = F.ctx()
  
      row = torch.tensor([1, 1, 2]).to(ctx)
      col = torch.tensor([2, 4, 3]).to(ctx)
      val = torch.randn(val_shape).to(ctx)
      A = from_coo(row, col, val)
      A_dense = A.to_dense()
  
      shape = A.shape + val.shape[1:]
      mat = torch.zeros(shape, device=ctx)
      mat[row, col] = val
      assert torch.allclose(A_dense, mat)
  
imports: import unittest
  import warnings
  import backend
  import pytest
  import torch
  from dgl.sparse import diag, from_coo, from_csc, from_csr, from_torch_sparse, identity, to_torch_sparse_coo, to_torch_sparse_csc, to_torch_sparse_csr, val_like
  from .utils import rand_coo, rand_csc, rand_csr, rand_diag, sparse_matrix_to_dense
label_desc: Use image node_id 5 for calling a global function with example usage: test_dense(val_shape) without return types