docstr: Test nn.Linear + .contiguous() + nn.ReLU with SparseSemiStructuredTensor + torch.compile
  We expect:
      (1) The sparse tensor subclass should turn nn.Linear into `aten._structured_sparse_linear` + `aten.contiguous()`
      (2) Inductor should fuse the .contiguous() call into the relu
docstr_tok: ["Test","nn.Linear","+",".contiguous","(",")","+","nn.ReLU","with","SparseSemiStructuredTensor","+","torch.compile","We","expect",":","(","1",")","The","sparse","tensor","subclass","should","turn","nn.Linear","into","`","aten._structured_sparse_linear","`","+","`","aten.contiguous","(",")","`","(","2",")","Inductor","should","fuse","the",".contiguous","(",")","call","into","the","relu"]
code: def _test_mlp_contiguous_relu_compile(backend, dense_input_shape):
      """
      Test nn.Linear + .contiguous() + nn.ReLU with SparseSemiStructuredTensor + torch.compile
      We expect:
          (1) The sparse tensor subclass should turn nn.Linear into `aten._structured_sparse_linear` + `aten.contiguous()`
          (2) Inductor should fuse the .contiguous() call into the relu
      """
  
      class Model(nn.Module):
          def __init__(self):
              super().__init__()
              self.linear = nn.Linear(128, 128)
  
          def forward(self, x):
              x = self.linear(x)
              x = x.contiguous()
              return torch.nn.functional.relu(x)
  
      SparseSemiStructuredTensor._FORCE_CUTLASS = backend == "cutlass"
  
      input = torch.rand(dense_input_shape, device="cuda").half()
      model = Model().eval().cuda().half()
      mod_linear = model.linear
      m, n = mod_linear.weight.shape
      mask = torch.Tensor([1, 0, 0, 1]).tile((m, n // 4)).bool().cuda()
      # set masked weight
      mod_linear.weight = nn.Parameter(mod_linear.weight * mask)
  
      dense_result = model(input)
      mod_linear.weight = nn.Parameter(
          to_sparse_semi_structured(mod_linear.weight)
      )
      sparse_result = model(input)
  
      model = torch.compile(model, backend="inductor", fullgraph=True)
      sparse_compile_result = model(input)
  
      # test that sparse_compile_result and dense_result are numerically close
      assert torch.allclose(
          dense_result, sparse_compile_result, rtol=1e-3, atol=1e-3
      )
      # assert sparse and sparse_compile have the same strides,
      # as meta registrations may return contiguous tensors when the output is transposed
      # https://github.com/pytorch/pytorch/pull/114477
      assert sparse_result.stride() == sparse_compile_result.stride()
  
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 the SparseSemiStructuredTensorCompileTest obj's underlying member method code with example usage: obj._test_mlp_contiguous_relu_compile(backend, dense_input_shape) and returns: torch