code: def _sdd_matmul(
      a,
      b,
      trans_a,
      trans_b,
      trans_c,
      spdims,
      block,
      luts,
      num_locks,
      widths,
      packs,
      bench,
      time,
  ):
      if trans_c:
          a, b = b, a
          trans_a, trans_b = not trans_b, not trans_a
      AS0 = a.size(0)
      # Shape check
      a_dim = -2 if trans_a else -1
      b_dim = -1 if trans_b else -2
      a_inner, b_inner = a.shape[a_dim], b.shape[b_dim]
      if a_inner != b_inner:
          raise ValueError(
              f"Size of tensor A along the {a_dim} dim ({a_inner}) must match size "
              f"of tensor B along the {b_dim} dim ({b_inner})"
          )
      if a_inner % 16 != 0:
          raise ValueError(
              "Reduction size for SDD must be a multiple of 16"
          )
  
      batch_size = a.size(0)
      a_outer = a.size(3 if trans_a else 2)
      dtype = a.dtype
      is_16_multiple = a_inner % 16 == 0
      is_32_multiple = a_inner % 32 == 0
      is_64_multiple = a_inner % 64 == 0
      if not is_16_multiple:
          raise ValueError(
              "Reduction size for SDD must be a multiple of 16"
          )
      device = a.device
      # create kernel
      total_width = sum(
          [width * pack * pack for width, pack in zip(widths, packs)]
      )
      c = torch.empty(
          (batch_size, total_width, block, block),
          dtype=dtype,
          device=a.device,
      )
      for lut, width, pack in zip(luts, widths, packs):
          F32TK = [8, 16]
          F16TK = [16]
          F16TK += [32] if is_32_multiple else []
          F16TK += [64] if is_64_multiple else []
          TK = {torch.float32: F32TK, torch.float16: F16TK}[dtype]
          num_lock = 1
          meta = {
              "TM": block * pack,
              "TN": block * pack,
              "BLOCK": block,
              "TK": TK[0],
              "TZ": 1,
              "SDD": True,
              "DSD": False,
              "DDS": False,
          }
          # create output
          locks = _sparse_matmul.get_locks(
              2 * width * AS0 * num_lock, a.device
          )
          # maximum grid size is 65535
          # so operation might be decomposed into multiple
          # kernel calls
          max_width = 49152
          total = 0 if bench else None
          for off_width in range(0, width, max_width):
              grid = lambda meta: [
                  meta["TZ"],
                  min(max_width, width - off_width),
                  batch_size,
              ]
              _kernel[grid](
                  a,
                  b,
                  c,
                  a.stride(0),
                  a.stride(1),
                  a.stride(3 if trans_a else 2),
                  a.stride(2 if trans_a else 3),
                  b.stride(0),
                  b.stride(1),
                  b.stride(3 if trans_b else 2),
                  b.stride(2 if trans_b else 3),
                  c.stride(0),
                  c.stride(0),
                  c.stride(2),
                  c.stride(3),
                  a_outer,
                  a_outer,
                  a_inner,
                  off_width,
                  lut,
                  locks,
                  num_lock,
                  num_warps=4,
                  **meta,
              )
      # save for backward pass
      return c
  
imports: import importlib
  import torch
  import triton
  import triton.language
  import triton._C.libtriton
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 4 for calling the _sparse_matmul obj's underlying member method code with example usage: obj._sdd_matmul(a, b, trans_a, trans_b, trans_c, spdims, block, luts, num_locks, widths, packs, bench, time) and returns: c