t.Errorf("#%d got %s want %s", i, x.FloatString(test.prec), test.out)
		}
	}
}

// Test inputs to Rat.SetString. The prefix "long:" causes the test
// to be skipped except in -long mode.  (The threshold is about 500us.)
var float64inputs = []string{
	// Constants plundered from strconv/testfp.txt.

	// Table 1: Stress Inputs for Conversion to 53-bit Binary, < 1/2 ULP
	"5e+125",
	"69e+267",
	"999e-026",
	"7861e-034",

		input string
	}

	testCases := []struct {
		name     string
		inputs   []inputStruct
		expected map[int64]int64
	}{
		{
			name: "no valid hugepages",
			inputs: []inputStruct{
				{
					key:   "2Mi",
					input: "128",
				},
			},
			expected: map[int64]int64{},
		},
		{
			name: "2Mi only",
			inputs: []inputStruct{
				{
					key:   v1.ResourceHugePagesPrefix + "2Mi",

        }
    })
    return properties
}


private
fun collectRegisteredInputsOf(task: Task): List<RegisteredProperty> {

    val properties = mutableListOf<RegisteredProperty>()

    (task.inputs as TaskInputsInternal).visitRegisteredProperties(object : PropertyVisitor {

        override fun visitInputFileProperty(
            propertyName: String,
            optional: Boolean,

  // sc_forward should have TPU ops including replicated outputs but not inputs
  // CHECK: func.func private @_func_sc_forward
  // CHECK-NOT: TPUReplicatedInput
  // CHECK-DAG: TPUReplicateMetadata
  // CHECK-DAG: TPUCompilationResult
  // CHECK-DAG: TPUReplicatedOutput
  // CHECK: return

  // core_tput should have TPU ops including both replicated inputs and outputs
  // CHECK: func.func private @_func_core_tpu

LogicalResult VerifyNgrams(func::FuncOp func) {
  // The inputs and outputs should be the same:
  // * A string tensor for tokens/ragged tensor values.
  // * Zero or more row_split tensors.
  constexpr int kValues = 0;
  constexpr int kRowSplits = 1;

  if (func.getFunctionType().getInputs().size() !=
      func.getFunctionType().getResults().size()) {
    return func.emitError() << "Mismatched number of inputs and outputs.";
  }

  %r = "tf.AddV2"(%w, %v) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %r : tensor<i32>
}

// -----

// Test inputs to the child streams are merged to the parent streams

// CHECK-LABEL: func private @main_stream_1
// CHECK-SAME: ([[INPUT0:%.*]]: tensor<i32>, [[INPUT1:%.*]]: tensor<i32>
// CHECK: tf.Sub
// CHECK: tf.Sub
// CHECK: mlrt.async({{%.*}}, [[INPUT1]]

// CHECK-LABEL: func @main

	values := file.AsStringOrFail(t, filepath.Join("testdata", "inputs", fname+".values.gen.yaml"))
	template := file.AsBytesOrFail(t, filepath.Join("testdata", "inputs", fname+".template.gen.yaml"))
	meshc := file.AsStringOrFail(t, filepath.Join("testdata", "inputs", fname+".mesh.gen.yaml"))

	vc, err := NewValuesConfig(values)
	if err != nil {
		t.Fatal(err)
	}

  llvm::SmallVector<llvm::StringRef, 4> function_input_names;
  function_input_names.reserve(model_flags.input_arrays().size());
  auto input_attr = tf_attrs.get("inputs");
  if (!input_attr) {
    return errors::InvalidArgument("no inputs attribute found");
  }
  auto input_names = mlir::cast<mlir::StringAttr>(input_attr).getValue();
  input_names.split(function_input_names, ",", /*MaxSplit=*/-1,

// CHECK-EMPTY:
// CHECK-NEXT:       },
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     } ],
// CHECK-NEXT:     inputs: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 ],
// CHECK-NEXT:     outputs: [ 24 ],
// CHECK-NEXT:     operators: [ {
// CHECK-NEXT:       inputs: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 22, 23, 18, 19, 20, 21 ],
// CHECK-NEXT:       outputs: [ 24 ],

// CHECK-EMPTY:
// CHECK-NEXT:       },
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     } ],
// CHECK-NEXT:     inputs: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 ],
// CHECK-NEXT:     outputs: [ 24 ],
// CHECK-NEXT:     operators: [ {
// CHECK-NEXT:       inputs: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 22, 23, 18, 19, 20, 21 ],
// CHECK-NEXT:       outputs: [ 24 ],