assert values.length > 0;
        return new TestProvider<>(Cast.uncheckedNonnullCast(values[0].getClass()), Arrays.asList(values), null);
    }

    public static <T> ProviderInternal<T> withChangingExecutionTimeValues(T... values) {
        assert values.length > 0;
        return new TestProviderWithChangingValue<>(Cast.uncheckedNonnullCast(values[0].getClass()), Arrays.asList(values), null);
    }

    // Check if the same-scale requirement is met.
    auto dq_arg_type = preceding_sc_op.getArg().getType().cast<TensorType>();
    auto qtype = dq_arg_type.getElementType().cast<QuantizedType>();
    auto q_result_type = sc_op.getType().cast<TensorType>();
    auto out_qtype = q_result_type.getElementType().cast<QuantizedType>();
    if (qtype != out_qtype) {
      avg_pool_op.emitError(

            MapProperty<Object, Object> asMapProperty = Cast.uncheckedNonnullCast(target);
            DefaultProvider<Map<Object, Object>> convention = new DefaultProvider<>(() -> Cast.uncheckedNonnullCast(mapping.getValue(_convention, _source)));
            asMapProperty.convention(convention);
        } else if (target instanceof HasMultipleValues) {
            HasMultipleValues<Object> asCollectionProperty = Cast.uncheckedNonnullCast(target);

            return Cast.uncheckedCast(new NoDescriptionValuesJustReturningParser());
        } else if (targetType.isAssignableFrom(JavaVersion.class)) {
            NotationConverter<CharSequence, JavaVersion> converter = new JavaVersionFromCharSequenceNotationConverter();
            return Cast.uncheckedCast(new NotationConverterToNotationParserAdapter<>(converter));

// Attribute Constraint that checks if the attribute value is zero.
def ZeroIntAttr
  : AttrConstraint<CPred<"$_self.cast<::mlir::IntegerAttr>().getInt() == 0">>;

// Checks if the value has rank at most 'n'.
class HasRankAtLeast<int n> : Constraint<
    CPred<"$0.getType().cast<ShapedType>().hasRank() && "
          "$0.getType().cast<ShapedType>().getRank() >= " # n>>;

// Checks value is not produced by a TFL_Quant or

  transpose_op++;
  EXPECT_EQ(mlir::cast<RankedTensorType>(transpose_op->getOperand(0).getType())
                .getDimSize(0),
            3);
  EXPECT_EQ(mlir::cast<RankedTensorType>(transpose_op->getOperand(0).getType())
                .getDimSize(1),
            12);
  EXPECT_EQ(mlir::cast<RankedTensorType>(transpose_op->getResult(0).getType())
                .getDimSize(0),

// Ensure cast with bfloat16 roundtrip exactly

func.func @main(tensor<4x5xbf16>) -> tensor<4x5xbf16> {
^bb0(%arg0: tensor<4x5xbf16>):
  // CHECK-LABEL: @main
  // CHECK:  (tensor<4x5xbf16>) -> tensor<4x5xf32>
  // CHECK-NEXT:  (tensor<4x5xf32>) -> tensor<4x5xbf16>
  %0 = "tfl.cast" (%arg0) : (tensor<4x5xbf16>) -> tensor<4x5xf32> loc("cast1")
  %1 = "tfl.cast" (%0) : (tensor<4x5xf32>) -> tensor<4x5xbf16> loc("cast2")

        %input_i32 = "tf.Cast"(%weight) : (tensor<1024x24x24x3xi8>) -> tensor<1024x24x24x3xi32>
        %output = "tf.Sub"(%input_i32, %zp) : (tensor<1024x24x24x3xi32>, tensor<i32>) -> tensor<1024x24x24x3xi32>
        %cast = "tf.Cast"(%output) : (tensor<1024x24x24x3xi32>) -> tensor<1024x24x24x3xf32>
        %mul = "tf.Mul"(%cast, %scale) : (tensor<1024x24x24x3xf32>, tensor<f32>) -> tensor<1024x24x24x3xf32>

            details == 'redundant cast to java.lang.String'
            solutions.empty
            verifyAll(getSingleLocation(ReceivedProblem.ReceivedFileLocation)) {
                it.path == fooFileLocation.absolutePath
            }
            additionalData.asMap ["formatted"] == """\
$fooFileLocation:5: warning: [cast] redundant cast to $expectedType
        String s = (String)"Hello World";

      new_unit.set_node_name(name_loc_id.str());
    }
  };

  ArrayRef<Location> locations = mlir::cast<FusedLoc>(loc).getLocations();
  if (IsImportLocPattern(mlir::cast<FusedLoc>(loc))) {
    QuantizationUnit new_unit;
    // Op type is a NameLoc with the ":" suffix.
    StringRef op_type_with_suffix =
        mlir::cast<NameLoc>(locations.front()).getName().strref();
    StringRef op_type =