code: def matmul_kernel(
      a_ptr,
      b_ptr,
      z_ptr,  #
      M,
      N,
      K,  #
      stride_am,
      stride_ak,  #
      stride_bk,
      stride_bn,  #
      stride_zm,
      stride_zn,  #
      BLOCK_SIZE_M: tl.constexpr,
      BLOCK_SIZE_N: tl.constexpr,
      BLOCK_SIZE_K: tl.constexpr,
      GROUP_SIZE_M: tl.constexpr,  #
      A_ORDER_0: tl.constexpr,
      A_ORDER_1: tl.constexpr,  #
      B_ORDER_0: tl.constexpr,
      B_ORDER_1: tl.constexpr,  #
  ):
      pid = tl.program_id(axis=0)
      num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
      num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
      num_pid_in_group = GROUP_SIZE_M * num_pid_n
      group_id = pid // num_pid_in_group
      first_pid_m = group_id * GROUP_SIZE_M
      group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
      pid_m = first_pid_m + (pid % group_size_m)
      pid_n = (pid % num_pid_in_group) // group_size_m
      block_offset_m = pid_m * BLOCK_SIZE_M
      block_offset_n = pid_n * BLOCK_SIZE_N
  
      a_tile_ptr = tl.make_block_ptr(
          base=a_ptr,
          shape=(M, K),
          strides=(stride_am, stride_ak),
          offsets=(block_offset_m, 0),
          block_shape=(BLOCK_SIZE_M, BLOCK_SIZE_K),
          order=(A_ORDER_0, A_ORDER_1),
      )
      b_tile_ptr = tl.make_block_ptr(
          base=b_ptr,
          shape=(K, N),
          strides=(stride_bk, stride_bn),
          offsets=(0, block_offset_n),
          block_shape=(BLOCK_SIZE_K, BLOCK_SIZE_N),
          order=(B_ORDER_0, B_ORDER_1),
      )
      z = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
  
      offs_m = block_offset_m + tl.arange(0, BLOCK_SIZE_M)
      offs_n = block_offset_n + tl.arange(0, BLOCK_SIZE_N)
      z_ptrs = (
          z_ptr
          + offs_m[:, None] * stride_zm
          + offs_n[None, :] * stride_zn
      )
      mask = (offs_m < M)[:, None] & (offs_n < N)[None, :]
  
      for k in range(0, K, BLOCK_SIZE_K):
          a = tl.load(a_tile_ptr)
          b = tl.load(b_tile_ptr)
          z += tl.dot(a, b)
          a_tile_ptr = tl.advance(a_tile_ptr, [0, BLOCK_SIZE_K])
          b_tile_ptr = tl.advance(b_tile_ptr, [BLOCK_SIZE_K, 0])
  
      z = z.to(tl.float16)
  
      tl.store(z_ptrs, z, mask=mask)
  
imports: import torch
  from torch.testing import assert_close
  import triton
  import triton.language
label_desc: Use image node_id 1 for calling a global function with example usage: matmul_kernel(a_ptr, b_ptr, z_ptr, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride_zm, stride_zn, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K, GROUP_SIZE_M, A_ORDER_0, A_ORDER_1, B_ORDER_0, B_ORDER_1) without return types