code: def warp_specialized_matmul_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,  #
  ):
      tid = tl.program_id(axis=0)
      n_tiles = tl.cdiv(N, BLOCK_N)
      pid_m = tid // n_tiles
      pid_n = tid % n_tiles
  
      offs_k = tl.arange(0, BLOCK_K)
      rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
      rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
      offs_am = tl.max_contiguous(
          tl.multiple_of(rm % M, BLOCK_M), BLOCK_M
      )
      offs_bn = tl.max_contiguous(
          tl.multiple_of(rn % N, BLOCK_N), BLOCK_N
      )
      a_ptrs = a_ptr + (
          offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
      )
      b_ptrs = b_ptr + (
          offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn
      )
  
      accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
  
      for k in range(0, K, BLOCK_K):
          a = tl.load(a_ptrs)
          b = tl.load(b_ptrs)
          accumulator += tl.dot(a, b)
          a_ptrs += BLOCK_K * stride_ak
          b_ptrs += BLOCK_K * stride_bk
      accumulator = accumulator.to(c_ptr.dtype.element_ty)
  
      offs_cm = tl.arange(0, BLOCK_M) + pid_m * BLOCK_M
      offs_cn = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N
  
      c_ptrs = (
          c_ptr
          + offs_cm[:, None] * stride_cm
          + offs_cn[None, :] * stride_cn
      )
      mask = (offs_cm < M)[:, None] & (offs_cn < N)[None, :]
      tl.store(c_ptrs, accumulator, mask=mask)
  
imports: import itertools
  import pytest
  import torch
  from torch.testing import assert_close
  import triton
  import triton.language
  from triton.runtime import driver
label_desc: Use image node_id 5 for calling a global function with example usage: warp_specialized_matmul_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