code: def test_block_copy(dtypes_str, n, padding_option):
      capability = torch.cuda.get_device_capability()
      if capability[0] >= 9:
          pytest.skip("Hopper support is working in progress")
  
      src_dtype_str = dtypes_str[0]
      dst_dtype_str = dtypes_str[0]
      src_dtype = getattr(torch, src_dtype_str)
      dst_dtype = getattr(torch, dst_dtype_str)
      if src_dtype_str in ("bool", "int16"):
          if padding_option == "nan":
              pytest.skip(
                  "Padding with NaN is not supported for integer types"
              )
          a = torch.randint(0, 2, (n,), device="cuda", dtype=src_dtype)
      else:
          a = torch.randn((n,), device="cuda", dtype=src_dtype)
      b = torch.zeros((n,), device="cuda", dtype=dst_dtype)
  
      grid = lambda meta: (triton.cdiv(n, meta["BLOCK_SIZE"]),)
      block_copy_kernel[grid](
          a_ptr=a,
          b_ptr=b,
          N=n,
          BLOCK_SIZE=64,
          padding_option=padding_option,
      )
      a.to(dst_dtype)
      assert torch.all(a[0 : n // 2] == b[0 : n // 2])
      if padding_option == "zero":
          assert torch.all(b[n // 2 : n] == 0)
      else:
          assert torch.all(torch.isnan(b[n // 2 : n]))
  
imports: import pytest
  import torch
  import triton
  import triton.language
label_desc: Use image node_id 2 for calling a global function with example usage: test_block_copy(dtypes_str, n, padding_option) without return types