}
	panic("shift too large")
}

func provedUnsignedShiftLeft(val64 uint64, val32 uint32, val16 uint16, val8 uint8, shift int) (r1 uint64, r2 uint32, r3 uint16, r4 uint8) {
	if shift >= 0 && shift < 64 {
		// arm64:"LSL",-"CSEL"
		r1 = val64 << shift
	}
	if shift >= 0 && shift < 32 {
		// arm64:"LSL",-"CSEL"
		r2 = val32 << shift
	}
	if shift >= 0 && shift < 16 {
		// arm64:"LSL",-"CSEL"

			f.ByteOrder.PutUint64(dst[rela.Off:rela.Off+8], val64)
		case R_X86_64_32:
			if rela.Off+4 >= uint64(len(dst)) || rela.Addend < 0 {
				continue
			}
			val32 := uint32(sym.Value) + uint32(rela.Addend)
			f.ByteOrder.PutUint32(dst[rela.Off:rela.Off+4], val32)
		}
	}

	return nil
}

func (f *File) applyRelocations386(dst []byte, rels []byte) error {
	// 8 is the size of Rel32.
	if len(rels)%8 != 0 {

// CHECK-DAG:       %[[VAL_2:.*]] = "tfl.pseudo_const"{{.*}}dense<1> : tensor<4xi32>
// CHECK-DAG:       %[[VAL_3:.*]] = "tfl.pseudo_const"{{.*}}dense<2> : tensor<1xi32>
// CHECK-DAG:       %[[VAL_4:.*]] = "tfl.pseudo_const"{{.*}}dense<[2, 1]> : tensor<2xi32>
// CHECK:           %[[VAL_5:.*]] = "tfl.reshape"(%[[VAL_0]], %[[VAL_2]]) : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>

// CHECK:           %[[VAL_2:.*]] = "tfl.pack"(%[[VAL_0]], %[[VAL_1]]) <{axis = 0 : i32, values_count = 2 : i32}> {tac.device = "CPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>
// CHECK:           return %[[VAL_2]] : tensor<2x1xf32>
// CHECK:         }

kind: bar
metadata:
  name: no-errors
spec:
  field1: val1
  field2: val2
  field2: val3
  nested:
  - name: nestedName
    nestedField1: val1
    nestedField2: val2
    nestedField2: val3`
	testCases := []struct {
		name     string
		yaml     string
		expected []string
	}{
		{
			name: "no errors",
			yaml: yamlNoErrs,
		},
		{
			name:     "one error",

  func.return %0 : tensor<1x384x32xf32>
}

// PARTIAL:       func @simple_test(%[[VAL_0:.*]]: tensor<4x384x32xf32>, %[[VAL_1:.*]]: tensor<3xi32>, %[[VAL_2:.*]]: tensor<3xi32>) -> tensor<1x384x32xf32> attributes {tac.interface_name = "func1"} {
// PARTIAL:           %[[VAL_3:.*]] = "tfl.pseudo_const"() <{value = dense<[1, 384, 32]> : tensor<3xi32>}> : () -> tensor<3xi32>

// CHECK:           %[[VAL_1:.*]] = "tfl.hard_swish"(%[[VAL_0]]) : (tensor<2xf32>) -> tensor<2xf32>
// CHECK:           %[[VAL_2:.*]] = "tf.Identity"(%[[VAL_1]]) {device = ""} : (tensor<2xf32>) -> tensor<*xf32>
// CHECK:           %[[VAL_3:.*]] = "tf.Identity"(%[[VAL_2]]) {device = ""} : (tensor<*xf32>) -> tensor<*xf32>
// CHECK:           return %[[VAL_3]] : tensor<*xf32>
// CHECK:         }

		var lower, higher uint64
		val1 := n.AuxUnsigned()
		for val2, ptr := range po.constants {
			val2 := uint64(val2)
			if val1 == val2 {
				panic("unreachable")
			}
			if val2 < val1 && (lowerptr == 0 || val2 > lower) {
				lower = val2
				lowerptr = ptr
			} else if val2 > val1 && (higherptr == 0 || val2 < higher) {
				higher = val2
				higherptr = ptr
			}
		}
	} else {

};

// Check if given indices in `val1` has same number of elements as given
// indices in `val2`.
bool HasEqualElementSize(Value val1, Value val2, ArrayRef<int> val1_indices,
                         ArrayRef<int> val2_indices) {
  ShapedType val1_shape = mlir::cast<ShapedType>(val1.getType());
  ShapedType val2_shape = mlir::cast<ShapedType>(val2.getType());
  if (!val1_shape.hasRank() || !val2_shape.hasRank()) return false;

        ModuleComponentSelector selector2 = DefaultModuleComponentSelector.newSelector(
            DefaultModuleIdentifier.newId('group2', 'name2'), constraint('1.0'), AttributeTestUtil.attributes("foo": "val2"), [])
        ModuleComponentSelector selector3 = DefaultModuleComponentSelector.newSelector(
            DefaultModuleIdentifier.newId('group3', 'name3'), constraint('1.0'), AttributeTestUtil.attributes("foo": "val1"), [])