code: def test_inline_multi_reduce():
      def argmax_comp(x, y):
          idx = tvm.tir.Select((x[1] >= y[1]), x[0], y[0])
          val = tvm.tir.Select((x[1] >= y[1]), x[1], y[1])
          return idx, val
  
      def argmax_init(idx_typ, val_typ):
          return tvm.tir.const(-1, idx_typ), tvm.te.min_value(val_typ)
  
      argmax = te.comm_reducer(argmax_comp, argmax_init, name="argmax")
      m = te.var("m")
      n = te.var("n")
      val = te.placeholder((m, n), name="val", dtype="float32")
      val1 = te.compute((m, n), lambda i, j: val[i, j] + 1, name="val1")
      val2 = te.compute(
          (m, n), lambda i, j: te.exp(val1[i, j]), name="val2"
      )
      k = te.reduce_axis((0, n), "k")
      T_idx, T_val = te.compute(
          (m,), lambda i: argmax((k.var, val2[i, k]), axis=k), name="T"
      )
      s = te.create_schedule(T_idx.op)
      s[val1].compute_inline()
      s = s.normalize()
      bounds = tvm.te.schedule.InferBound(s)
      stmt = tvm.te.schedule.ScheduleOps(s, bounds)
  
imports: import numpy
  import tvm
  from tvm import te
  from tvm.driver.build_module import schedule_to_module
label_desc: Use image node_id 6 for calling a global function with example usage: test_inline_multi_reduce() and returns: idx,  val, tvm,  tvm