return ExpressedToQuantizedConverter{input_type, element_type};
  }
  // Supported primitive type (which just is the expressed type).
  if (isQuantizablePrimitiveType(input_type))
    return ExpressedToQuantizedConverter{input_type, input_type};
  // Unsupported.
  return ExpressedToQuantizedConverter{input_type, nullptr};
}

Type ExpressedToQuantizedConverter::convert(

    if (input_op && input_op == from_graph.getOperation()) {
      auto input_type =
          mlir::dyn_cast_or_null<RankedTensorType>(input.getType());
      if (input_type == nullptr || !input_type.hasStaticShape()) continue;
      // Quantized type does not support getSizeInBits.
      if (IsQUI8Type(input_type) || IsQI8Type(input_type)) {
        total_size_transferred += input_type.getNumElements() * 8;
      } else {

    int64_t total_count = 0;
    for (auto input : op->getOperands()) {
      auto input_type =
          mlir::dyn_cast_or_null<mlir::RankedTensorType>(input.getType());
      if (!input_type || !input_type.hasStaticShape()) {
        return false;
      }
      total_count += input_type.getNumElements();
    }
    *count = total_count;
    return true;
  }

 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(ModifyIONodesPass)

  explicit ModifyIONodesPass() {}
  explicit ModifyIONodesPass(mlir::Type input_type, mlir::Type output_type) {
    this->input_type = input_type;
    this->output_type = output_type;
  }

  void runOnOperation() override;

 private:
  // Assign the io types from the command line flag. This is only required for
  // tests.

                            TF_ShapeHandle* shape, TF_DataType input_type,
                            TF_DataType output_type, TF_Status* status) {
  size_t input_type_size = TF_DataTypeSize(input_type);
  size_t output_type_size = TF_DataTypeSize(output_type);

  if (input_type_size == 0 || output_type_size == 0) {
    std::ostringstream err;
    err << "Cannot bitcast type " << input_type << " to " << output_type

  bool uint8_type_observed = false;
  for (auto& input : op->getOpOperands()) {
    auto input_type = input.get().getType();
    if (IsF32ShapedType(input_type)) {
      float_type_observed = true;
    } else if (IsQI8Type(input_type)) {
      int8_type_observed = true;
    } else if (IsQUI8Type(input_type)) {
      uint8_type_observed = true;
    }
  }

  // We should not observe both uint8 & int8.

    SmallVector<Value, 4> dequantized_inputs;
    for (auto& input : op->getOpOperands()) {
      auto input_type = input.get().getType();
      if (IsQI8Type(input_type) || IsQUI8Type(input_type) ||
          IsQI32Type(input_type)) {
        auto dequantized_input_type =
            mlir::quant::QuantizedType::castToExpressedType(input_type);
        builder->setInsertionPoint(op);
        auto dequantize_op = builder->create<TFL::DequantizeOp>(

  auto input_type = RankedTensorType::get({1, 2, 2, 1}, builder_->getF32Type());
  auto output_type =
      RankedTensorType::get({1, 2, 1, 1}, builder_->getF32Type());
  SmallVector<mlir::Type, 1> input_types{input_type};
  SmallVector<mlir::Type, 1> output_types{output_type};

  auto max_unpooling_func =
      createMaxUnpoolingFunc<1, 1>(builder_.get(), input_types, output_types);

  DataType input_type;
  string method;
  TF_RETURN_IF_ERROR(GetNodeAttr(op.node()->attrs(), "method", &method));
  TF_RETURN_IF_ERROR(GetNodeAttr(op.node()->attrs(), "T", &input_type));
  auto image_shape = Shape(scope, op.input(0));
  grad_outputs->push_back(CropAndResizeGradImage(
      scope, grad_inputs[0], op.input(1), op.input(2), image_shape, input_type,
      CropAndResizeGradImage::Method(method)));

    // can have smaller memory usage.
    auto input_type =
        mlir::dyn_cast<RankedTensorType>(dequantize_op.getOutput().getType());
    auto output_type = mlir::dyn_cast<RankedTensorType>(
        passthrough_op->getResult(0).getType());
    if (!input_type || !output_type ||
        get_num_elements(input_type) <= get_num_elements(output_type)) {
      return failure();
    }