code: def matmul_kernel_with_block_pointers(
      # Pointers to matrices
      a_ptr,
      b_ptr,
      c_ptr,
      # Matrix dimensions
      M,
      N,
      K,
      # The stride variables represent how much to increase the ptr by when moving by 1
      # element in a particular dimension. E.g. `stride_am` is how much to increase `a_ptr`
      # by to get the element one row down (A has M rows).
      stride_am,
      stride_ak,  #
      stride_bk,
      stride_bn,  #
      stride_cm,
      stride_cn,
      # Meta-parameters
      BLOCK_SIZE_M: tl.constexpr,
      BLOCK_SIZE_N: tl.constexpr,
      BLOCK_SIZE_K: tl.constexpr,
      GROUP_SIZE_M: tl.constexpr,
  ):
      """Kernel for computing the matmul C = A x B.
      A has shape (M, K), B has shape (K, N) and C has shape (M, N)
      """
      # -----------------------------------------------------------
      # Map program ids `pid` to the block of C it should compute.
      # This is done in a grouped ordering to promote L2 data reuse.
      # See the matrix multiplication tutorial for details.
      pid = tl.program_id(axis=0)
      num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
      num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
      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
  
      # ----------------------------------------------------------
      # Create block pointers for the first blocks of A and B.
      # We will advance this pointer as we move in the K direction and accumulate.
      # See above `Make a Block Pointer` section for details.
      a_block_ptr = tl.make_block_ptr(
          base=a_ptr,
          shape=(M, K),
          strides=(stride_am, stride_ak),
          offsets=(pid_m * BLOCK_SIZE_M, 0),
          block_shape=(BLOCK_SIZE_M, BLOCK_SIZE_K),
          order=(1, 0),
      )
      b_block_ptr = tl.make_block_ptr(
          base=b_ptr,
          shape=(K, N),
          strides=(stride_bk, stride_bn),
          offsets=(0, pid_n * BLOCK_SIZE_N),
          block_shape=(BLOCK_SIZE_K, BLOCK_SIZE_N),
          order=(1, 0),
      )
  
      # -----------------------------------------------------------
      # Iterate to compute a block of the C matrix.
      # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block.
      # of fp32 values for higher accuracy.
      # `accumulator` will be converted back to fp16 after the loop.
      accumulator = tl.zeros(
          (BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32
      )
      for k in range(0, K, BLOCK_SIZE_K):
          # Load with boundary checks, no need to calculate the mask manually.
          # For better performance, you may remove some axis from the boundary
          # check, if you can guarantee that the access is always in-bound in
          # that axis.
          # See above `Load/Store a Block Pointer` section for details.
          a = tl.load(a_block_ptr, boundary_check=(0, 1))
          b = tl.load(b_block_ptr, boundary_check=(0, 1))
          # We accumulate along the K dimension.
          accumulator += tl.dot(a, b)
          # Advance the block pointer to the next K block.
          # See above `Advance a Block Pointer` section for details.
          a_block_ptr = tl.advance(a_block_ptr, (0, BLOCK_SIZE_K))
          b_block_ptr = tl.advance(b_block_ptr, (BLOCK_SIZE_K, 0))
      c = accumulator.to(tl.float16)
  
      # ----------------------------------------------------------------
      # Write back the block of the output matrix C with boundary checks.
      # See above `Load/Store a Block Pointer` section for details.
      c_block_ptr = tl.make_block_ptr(
          base=c_ptr,
          shape=(M, N),
          strides=(stride_cm, stride_cn),
          offsets=(pid_m * BLOCK_SIZE_M, pid_n * BLOCK_SIZE_N),
          block_shape=(BLOCK_SIZE_M, BLOCK_SIZE_N),
          order=(1, 0),
      )
      tl.store(c_block_ptr, c, boundary_check=(0, 1))
  
imports: import torch
  import triton
  import triton.language
label_desc: Use image node_id 1 for calling a global function with example usage: matmul_kernel_with_block_pointers(a_ptr, b_ptr, c_ptr, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K, GROUP_SIZE_M) without return types