code: def _sample_points(batch_size, device, dtype=torch.float32):
      src = torch.tensor(
          [
              [
                  [0.0, 0.0],
                  [0.0, 10.0],
                  [10.0, 0.0],
                  [10.0, 10.0],
                  [5.0, 5.0],
              ]
          ],
          device=device,
          dtype=dtype,
      )
      src = src.repeat(batch_size, 1, 1)
      dst = src + torch.rand_like(src) * 2.5
      return src, dst
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  from kornia.testing import assert_close
  from kornia.utils._compat import torch_version_ge
label_desc: Use image node_id 1 for calling a global function with example usage: _sample_points(batch_size, device, dtype) and returns: src,  dst