code: def matmul_no_scf_with_advance_kernel(  #
      a_ptr,
      b_ptr,
      c_ptr,  #
      M,
      N,
      K,  #
      stride_am,
      stride_ak,  #
      stride_bk,
      stride_bn,  #
      stride_cm,
      stride_cn,  #
      BLOCK_M: tl.constexpr,
      BLOCK_N: tl.constexpr,
      BLOCK_K: tl.constexpr,  #
  ):
      offs_m = tl.arange(0, BLOCK_M)
      offs_n = tl.arange(0, BLOCK_N)
      a_block_ptr = tl.make_block_ptr(
          base=a_ptr,
          shape=(M, K),
          strides=(stride_am, stride_ak),
          offsets=(0, 0),
          block_shape=(BLOCK_M, BLOCK_K),
          order=(1, 0),
      )
      b_block_ptr = tl.make_block_ptr(
          base=b_ptr,
          shape=(K, N),
          strides=(stride_bk, stride_bn),
          offsets=(0, 0),
          block_shape=(BLOCK_K, BLOCK_N),
          order=(1, 0),
      )
      # Below two lines are just for testing negative offsets for the `advance` API, which could be removed
      a_block_ptr = tl.advance(a_block_ptr, (BLOCK_M, -BLOCK_K))
      a_block_ptr = tl.advance(a_block_ptr, (-BLOCK_M, BLOCK_K))
      a = tl.load(
          a_block_ptr, boundary_check=(1,), padding_option="zero"
      )
      b = tl.load(
          b_block_ptr, boundary_check=(0,), padding_option="zero"
      )
  
      c = tl.dot(a, b)
      c_ptrs = (
          c_ptr
          + offs_m[:, None] * stride_cm
          + offs_n[None, :] * stride_cn
      )
      tl.store(c_ptrs, c)
  
imports: import pytest
  import torch
  import triton
  import triton.language
label_desc: Use image node_id 3 for calling a global function with example usage: matmul_no_scf_with_advance_kernel(a_ptr, b_ptr, c_ptr, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, BLOCK_M, BLOCK_N, BLOCK_K) without return types