assertTrue(
        from.getType() + " is expected to be assignable to " + to.getType(),
        to.isSupertypeOf(from));
    assertTrue(
        to.getType() + " is expected to be a supertype of " + from.getType(),
        to.isSupertypeOf(from));
    assertTrue(
        from.getType() + " is expected to be a subtype of " + to.getType(), from.isSubtypeOf(to));
  }

bool IsI8ToF32Cast(stablehlo::ConvertOp convert_op) {
  const bool is_i8_operand =
      convert_op.getOperand().getType().getElementType().isInteger(/*width=*/8);
  const bool is_f32_result =
      mlir::isa<Float32Type>(convert_op.getResult().getType().getElementType());
  return is_i8_operand && is_f32_result;
}

// Tests whether a `stablehlo::ConvertOp` is a i8 -> i32 cast.

    assertTrue(
        from.getType() + " is expected to be assignable to " + to.getType(),
        to.isSupertypeOf(from));
    assertTrue(
        to.getType() + " is expected to be a supertype of " + from.getType(),
        to.isSupertypeOf(from));
    assertTrue(
        from.getType() + " is expected to be a subtype of " + to.getType(), from.isSubtypeOf(to));
  }

        loc, tensorflow::GetTypeFromTFTensorShape({}, rewriter.getI1Type()),
        size_diff, scalar_zero);

    // Build the argument/result types for if branch function.
    auto input_shape = rewriter.create<TF::ShapeOp>(
        loc, tensorflow::GetTypeFromTFTensorShape({-1}, shape_dtype),
        input_handle);

    Type branch_args_type[] = {input_handle.getType(), input_shape.getType(),

  "TFL::IsBroadcastableElementsAttrAndType($0.getType(), $1.getType())">>;

def OperandsBroadcastToOutputType : Constraint<CPred<
  "TFL::OperandsBroadcastToOutputType($0.getType(), $1.getType(), "
                                     "$2.getType())">>;

def IsTailOfShape : Constraint<CPred<
  "TFL::IsTailOfShape($0.getType(), $1.getType())">>;

def IsReducedTailOfShape : Constraint<CPred<

bool IsResource(Value value) {
  return mlir::isa<TF::ResourceType>(getElementTypeOrSelf(value.getType()));
}

// Get the type of the data contained in a resource. Returns null if there is
// no single type in the resource.
Type GetResourceSubtype(Value value) {
  auto resource_type =
      mlir::dyn_cast<TF::ResourceType>(getElementTypeOrSelf(value.getType()));
  auto subtypes = resource_type.getSubtypes();

        int type = be.getOperation().getType();
        if ((type == Types.ASSIGNMENT_OPERATOR || type == Types.EQUAL)
            && left instanceof ClassExpression) {
            ClassExpression ce = (ClassExpression) left;
            String error = "you tried to assign a value to the class '" + ce.getType().getName() + "'";
            if (ce.getType().isScript()) {

    auto input_type = mlir::dyn_cast_or_null<RankedTensorType>(input.getType());
    if (!input_type) return failure();
    auto fft_len = rfft_op.getFftLength();
    auto fft_len_type = mlir::dyn_cast_or_null<ShapedType>(fft_len.getType());
    if (!fft_len_type) return failure();

    auto output_type =
        mlir::dyn_cast_or_null<RankedTensorType>(rfft_op.getResult().getType());
    if (!output_type) return failure();

    auto loc = op.getLoc();
    auto result_ty = mlir::cast<ShapedType>(op.getType());

    auto input = op.getImages();
    auto input_ty = mlir::cast<ShapedType>(input.getType());
    auto input_element_ty = input_ty.getElementType();
    auto out_size = op.getSize();
    auto out_size_ty = mlir::cast<ShapedType>(out_size.getType());
    auto out_size_element_ty = out_size_ty.getElementType();

    new_quantized_func.setType(
        FunctionType::get(getContext(), TypeRange{ValueRange{args}},
                          new_quantized_func.getResultTypes()));
    for (auto [partitioned_call_arg, new_quantized_func_arg] :
         llvm::zip_equal(args, new_quantized_func.getArguments())) {
      new_quantized_func_arg.setType(partitioned_call_arg.getType());
    }