code: def get_mma_schedule():
      i_factors, j_factors, k_factors = (
          [1, 32, 1, 4, 2],
          [16, 2, 4, 1, 2],
          [128, 2, 1],
      )
  
      def index_map(i, j):
          return (
              i // 16,
              j // 16,
              *shared_16x16_to_ldmatrix_32x8_layout(i % 16, j % 16),
          )
  
      workload = te.create_prim_func(
          te_workload.matmul(
              N, M, K, in_dtype="float16", out_dtype="float32"
          )
      )
  
      return mma_schedule(
          workload,
          16,
          "float16",
          False,
          i_factors,
          j_factors,
          k_factors,
          index_map,
          index_map,
          index_map,
          LDMATRIX_f16_A_DYN_INTRIN,
          LDMATRIX_f16_B_DYN_INTRIN,
          MMA_f16f16f32_INTRIN,
          MMA_fill_16x16_f32_INTRIN,
          MMA_store_16x16_f32_global_INTRIN,
          "shared.dyn",
      )
  
imports: import sys
  import numpy
  import pytest
  import tvm
  import tvm.testing
  import tvm.tir.tensor_intrin.cuda
  from tvm import TVMError, te, tir
  from tvm.meta_schedule.testing import te_workload
  from tvm.script import tir
  from tvm.testing.tir import mma_schedule
  from tvm.tir.tensor_intrin.cuda import LDMATRIX_f16_A_DYN_INTRIN, LDMATRIX_f16_B_DYN_INTRIN, MMA_f16f16f32_INTRIN, MMA_fill_16x16_f32_INTRIN, MMA_store_16x16_f32_global_INTRIN, shared_16x16_to_ldmatrix_32x8_layout
label_desc: Use image node_id 42 for calling a global function with example usage: get_mma_schedule() and returns: mma_schedule, unknown,  unknown,  unknown