args: []string{"debugdump"},
		},
		{
			tag:  "percent",
			args: []string{"percent"},
		},
	}

	for _, x := range scenarios {
		ins := fmt.Sprintf("-i=%s", empty)
		args := append([]string{}, x.args...)
		args = append(args, ins)
		if false {
			t.Logf("cmd is %s %v\n", s.tool, args)
		}
		cmd := testenv.Command(t, s.tool, args...)
		b, err := cmd.CombinedOutput()
		t.Logf("%% output: %s\n", string(b))

  ^bb0(%barg1: tensor<i32>, %barg2: tensor<f32>):
    %8 = func.call @my_cond(%barg1, %barg2) : (tensor<i32>, tensor<f32>) -> tensor<i1>
    "tf.Yield"(%8, %barg1, %barg2) : (tensor<i1>, tensor<i32>, tensor<f32>) -> ()
  }, {
  ^bb0(%barg1: tensor<i32>, %barg2: tensor<f32>):
    %r1, %r2 = func.call @my_body(%barg1, %barg2) : (tensor<i32>, tensor<f32>) -> (tensor<i32>, tensor<f32>)

            "tf.Yield"(%cond) : (tensor<i1>) -> ()
        }, {
          ^bb0(%barg0: tensor<i32>, %barg1: !tf_res, %barg2: !tf_res):
            %new_values:3 = func.call @whileregion_body(%barg0, %barg1, %barg2) : (tensor<i32>, !tf_res,!tf_res) -> (tensor<i32>, !tf_res,!tf_res)
            "tf.Yield"(%new_values#0, %new_values#1, %new_values#2) : (tensor<i32>, !tf_res,!tf_res) -> ()

    },  {
      // CHECK: ^bb0(%arg1: tensor<i32>, %arg2: tensor<0xf32>)
      ^bb0(%barg0: tensor<i32>, %barg1: tensor<0xf32>, %barg2: tensor<!tf_type.resource<tensor<0xf32>>>):
        %2 = "tf.ReadVariableOp"(%barg2) : (tensor<!tf_type.resource<tensor<0xf32>>>) -> (tensor<0xf32>)
        "tf.Yield"(%barg0, %2, %barg2) : (tensor<i32>, tensor<0xf32>, tensor<!tf_type.resource<tensor<0xf32>>>) -> ()

  %while = "tf.WhileRegion"(%max_size) ({
    // CHECK: ^bb0(%[[BARG0:.*]]: tensor<i32>
    ^bb0(%barg0: tensor<i32>):
     // CHECK: "tf._SomeOp"(%[[BARG0]])
     %pred = "tf._SomeOp"(%barg0) : (tensor<i32>) -> tensor<i1>
    "tf.Yield"(%pred) : (tensor<i1>) -> ()
  }, {
    // CHECK: ^bb0(%[[BARG0:.*]]: tensor<i32>
    ^bb0(%barg0: tensor<i32>):

          "tf.Yield"(%0) : (tensor<i1>) -> ()
         },{
          ^bb0(%barg0: !tf_res, %barg1: !tf_res, %barg2: !tf_res):
          // expected-remark@below {{Result #0, ID 1 : Unknown}}
          %u0 = "tf._UnknownSideEffectingOp_"() : () -> !tf_res
          "tf.Yield"(%u0, %barg2, %barg1) : (!tf_res, !tf_res, !tf_res) -> ()

				// add partial list of type arguments, if any
				if i < len(atargs) {
					targs = append(targs, atargs[i]...)
				}
				// make sure targs and tparams have the same length
				for len(targs) < len(tparams) {
					targs = append(targs, nil)
				}
				genericArgs = append(genericArgs, i)
			}
		}
	}
	assert(len(tparams) == len(targs))

	// at the moment we only support implicit instantiations of argument functions

         %cond = "tf.NotEqual"(%carg1, %limit) : (tensor<i32>, tensor<i32>) -> tensor<i1>
         "tf.Yield"(%cond) : (tensor<i1>) -> ()
    },  {
      ^bb0(%barg0: tensor<f32>, %barg1: tensor<i32>):
        %one = arith.constant dense<1> : tensor<i32>
        %sub = "tf.Sub"(%barg1, %one) : (tensor<i32>, tensor<i32>) -> tensor<i32>
        %3 = "tf.StringToNumber"(%arg1) {out_type = f32} : (tensor<!tf_type.string>) -> tensor<f32>

    "tf.Yield"(%check) : (tensor<i1>) -> ()
    }, {
  ^bb0(%barg0: tensor<2x?x!tf_type.qint8>, %barg1: tensor<?x2x!tf_type.qint8>):
    %id = "tf.Identity"(%barg0) : (tensor<2x?x!tf_type.qint8>) -> tensor<2x?x!tf_type.qint8>
    "tf.Yield"(%id, %barg1) : (tensor<2x?x!tf_type.qint8>, tensor<?x2x!tf_type.qint8>) -> ()

	if len(targs) < n {
		targs2 := make([]Type, n)
		copy(targs2, targs)
		targs = targs2
	}
	// len(targs) == n

	// Continue with the type arguments we have. Avoid matching generic
	// parameters that already have type arguments against function arguments:
	// It may fail because matching uses type identity while parameter passing
	// uses assignment rules. Instantiate the parameter list with the type