def check_tensors_on_the_same_shared_memory(t1: torch.Tensor, t2: torch.Tensor):
    """Check if two tensors are on the same shared memory.

    This function copies a random tensor value to `t1` and checks whether `t2`
    holds the same random value and checks whether t2 is a distinct tensor
    object from `t1`. Their equality confirms that they are separate tensors
    that rely on the shared memory for their tensor value.
    """
    assert t1.data_ptr() != t2.data_ptr()
    old_t1 = t1.clone()
    v = torch.randint_like(t1, 100)
    t1[:] = v
    assert torch.equal(t1, t2)
    t1[:] = old_t1
Check if two tensors are on the same shared memory.

    This function copies a random tensor value to `t1` and checks whether `t2`
    holds the same random value and checks whether t2 is a distinct tensor
    object from `t1`. Their equality confirms that they are separate tensors
    that rely on the shared memory for their tensor value.
    
tests/python/pytorch/graphbolt/impl/test_fused_csc_sampling_graph.py