code: def forward(
      ctx,
      a,
      b,
      trans_a,
      trans_b,
      trans_c,
      mode,
      spdims,
      block,
      c_lut,
      c_num_locks,
      c_width,
      c_packs,
      c_bench,
      c_time,
      da_lut,
      da_num_locks,
      da_width,
      da_packs,
      da_bench,
      da_time,
      db_lut,
      db_num_locks,
      db_width,
      db_packs,
      db_bench,
      db_time,
  ):
      c = _sparse_matmul.fn[mode](
          a,
          b,
          trans_a,
          trans_b,
          trans_c,
          spdims,
          block,
          c_lut,
          c_num_locks,
          c_width,
          c_packs,
          c_bench,
          c_time,
      )
      # save for backward
      ctx.save_for_backward(a, b)
      ctx.da_num_locks = da_num_locks
      ctx.da_lut = da_lut
      ctx.da_width = da_width
      ctx.da_packs = da_packs
      ctx.da_bench = da_bench
      ctx.da_time = da_time
      ctx.db_lut = db_lut
      ctx.db_num_locks = db_num_locks
      ctx.db_width = db_width
      ctx.db_bench = db_bench
      ctx.db_packs = db_packs
      ctx.db_time = db_time
      ctx.mode = mode
      ctx.spdims = spdims
      ctx.block = block
      ctx.trans_a = trans_a
      ctx.trans_b = trans_b
      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 8 for calling the _sparse_matmul obj's underlying member method code with example usage: obj.forward(ctx, a, b, trans_a, trans_b, trans_c, mode, spdims, block, c_lut, c_num_locks, c_width, c_packs, c_bench, c_time, da_lut, da_num_locks, da_width, da_packs, da_bench, da_time, db_lut, db_num_locks, db_width, db_packs, db_bench, db_time) and returns: c