code: def test_zero_elements(reduce, dtype, device):
      x = torch.randn(
          0, 0, 0, 16, dtype=dtype, device=device, requires_grad=True
      )
      index = tensor([], torch.long, device)
      indptr = tensor([], torch.long, device)
  
      out = scatter(x, index, dim=0, dim_size=0, reduce=reduce)
      out.backward(torch.randn_like(out))
      assert out.size() == (0, 0, 0, 16)
  
      out = segment_coo(x, index, dim_size=0, reduce=reduce)
      out.backward(torch.randn_like(out))
      assert out.size() == (0, 0, 0, 16)
  
      out = gather_coo(x, index)
      out.backward(torch.randn_like(out))
      assert out.size() == (0, 0, 0, 16)
  
      out = segment_csr(x, indptr, reduce=reduce)
      out.backward(torch.randn_like(out))
      assert out.size() == (0, 0, 0, 16)
  
      out = gather_csr(x, indptr)
      out.backward(torch.randn_like(out))
      assert out.size() == (0, 0, 0, 16)
  
imports: from itertools import product
  import pytest
  import torch
  from torch_scatter import gather_coo, gather_csr, scatter, segment_coo, segment_csr
  from torch_scatter.testing import devices, grad_dtypes, reductions, tensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_zero_elements(reduce, dtype, device) without return types