code: def rand_sparse_semi_structured_all_patterns(r, c, dtype, device):
      pattern = "2by4" if dtype != torch.float32 else "1by2"
      if pattern == "1by2":
          ksparse = 2
          choices = [
              [[0, 0], [0, 1]],
              [[0, 1], [0, 1]],
              [[1, 0], [1, 0]],
              [[1, 1], [1, 0]],
          ]
      elif pattern == "2by4":
          ksparse = 4
          choices = [
              [[0, 0, 0, 0], [0, 0, 1, 1]],
              [[0, 0, 0, 1], [0, 0, 1, 1]],
              [[0, 0, 1, 0], [0, 0, 1, 1]],
              [[0, 0, 1, 1], [0, 0, 1, 1]],
              [[0, 1, 0, 0], [0, 1, 1, 0]],
              [[0, 1, 0, 1], [0, 1, 0, 1]],
              [[0, 1, 1, 0], [0, 1, 1, 0]],
              [[0, 1, 1, 1], [0, 1, 0, 1]],
              [[1, 0, 0, 0], [1, 0, 1, 0]],
              [[1, 0, 0, 1], [1, 0, 0, 1]],
              [[1, 0, 1, 0], [1, 0, 1, 0]],
              [[1, 0, 1, 1], [1, 0, 0, 1]],
              [[1, 1, 0, 0], [1, 1, 0, 0]],
              [[1, 1, 0, 1], [1, 1, 0, 0]],
              [[1, 1, 1, 0], [1, 1, 0, 0]],
              [[1, 1, 1, 1], [1, 1, 0, 0]],
          ]
      mask_rows = [
          random.randint(0, len(choices) - 1)
          for i in range(r * c // ksparse)
      ]
  
      COL_INV, COL_VAL = 0, 1
      mask_entries_inv = [choices[i][COL_INV] for i in mask_rows]
      mask_entries_val = [choices[i][COL_VAL] for i in mask_rows]
      mask_inv = (
          torch.tensor(mask_entries_inv, dtype=torch.bool)
          .view(r, c)
          .to(device)
      )
      mask_val = (
          torch.tensor(mask_entries_val, dtype=torch.bool)
          .view(r, c)
          .to(device)
      )
      dense = make_tensor(r, c, dtype=dtype, device=device)
      dense[
          dense == 0
      ] = 1  # To prevent zeros except where mask below applied.
      dense_inv = dense.masked_fill(~mask_inv, 0)
      dense_val = dense_inv.masked_fill(~mask_val, 0)
  
      return dense_inv, dense_val
  
imports: import itertools
  import random
  import unittest
  import torch
  from torch import nn
  from torch.sparse.semi_structured import _DTYPE_TO_SEMI_STRUCTURED_SPARSE_CONFIG, SparseSemiStructuredTensor, to_sparse_semi_structured
  from torch.testing import make_tensor
  from torch.testing._internal.common_device_type import dtypes, instantiate_device_type_tests
  from torch.testing._internal.common_dtype import all_types_and_complex
  import torch._dynamo.test_case
  from torch.testing._internal.common_utils import parametrize, run_tests, subtest, TestCase, TEST_WITH_ROCM, IS_WINDOWS
  from torch.utils._triton import has_triton
label_desc: Use image node_id 3 for calling a global function with example usage: rand_sparse_semi_structured_all_patterns(r, c, dtype, device) and returns: dense_inv,  dense_val