if (IsQI8Type(result_type) || IsQUI8Type(result_type)) {
        builder->setInsertionPoint(op);
        TFL::QuantizeOp quant_op = builder->create<TFL::QuantizeOp>(
            op->getLoc(), result_type, new_result, TypeAttr::get(result_type));
        new_result = quant_op.getResult();
      }

      // Rewire the outputs.
      result.replaceAllUsesWith(new_result);
    }

    // Remove the old op.
    op->erase();

// the casting, the quantization dimension of the result type needs to be set
// this new `axis` value.
TypeAttr CastQuantizedTypeAttrFromExpressedType(Builder builder,
                                                TypeAttr source, Type target,
                                                int axis);

// Quantizes the elements in the attribute `real_value` by the quantization

    builder.create<tf_saved_model::GlobalTensorOp>(
        NameLoc::get(builder.getStringAttr(name.str())),
        builder.getStringAttr(name), tensor_attr,
        TypeAttr::get(tensor_attr.getType()), builder.getUnitAttr());
  }

  return success();
}

}  // namespace

LogicalResult LiftVariables(ModuleOp module, Session* session) {

    sense of `tf.TensorShape` compatibility. And the element types must match.
  }];

  let arguments = (ins
    StrAttr:$sym_name,
    OptionalAttr<ElementsAttr>:$value,
    TypeAttr:$type,
    UnitAttr:$is_mutable
  );

  let hasVerifier = 1;
}

def TfSavedModel_SessionInitializerOp: TfSavedModel_Op<"session_initializer"> {
  let summary = "Initializes TensorFlow session state.";

        attr_type, attr_def.name, attr_def.type)
  else:
    attr_names = []
    if arg_def.number_attr:
      attr_names.append(arg_def.number_attr)
    if arg_def.type_attr:
      attr_names.append(arg_def.type_attr)
    if arg_def.type_list_attr:
      attr_names.append(arg_def.type_list_attr)

    # TODO(fengliuai): currently we don't support backward type inference, so we

      values.begin(), values.end(),
      [](const APInt& element_value) { return element_value.isNegative(); });
}

// Utility function to get the offset between two dense attribute values.
inline TypedAttr GetOffSet(Attribute begin, Attribute end) {
  auto begin_values = begin.cast<DenseElementsAttr>().getValues<int32_t>();
  auto end_values = end.cast<DenseElementsAttr>().getValues<int32_t>();

  SmallVector<int32_t> offsets;

    return f.isExactlyValue(value);
  });
}

// Returns true if `value` is compile-time constant and its splat value equals
// to `raw_value`.
template <typename T>
bool IsConstantValueOf(mlir::TypedAttr value, T raw_value) {
  auto element_type = mlir::cast<ShapedType>(value.getType()).getElementType();

  if (mlir::isa<FloatType>(element_type)) {
    return FloatValueEquals(value, raw_value);

          "mlir::OperandElementTypeIterator(values.end())};",
  [{
    ArrayAttr::get($_ctxt,
    [&]() {
      llvm::SmallVector<Attribute, 4> ret;
      for (auto t : $_self)
        ret.push_back(TypeAttr::get(t));
      return ret;
    }())
  }]
>;

// A derived attribute that returns the shapes of the tensors in the actual
// value pack that corresponds to the `idx`-th ODS-declared variadic operand.

  if (node.IsArg()) {
    if (dtype == DT_RESOURCE) {
      const AttrValue* dtype_attr = node.attrs().Find("_handle_dtypes");
      const AttrValue* shape_attr = node.attrs().Find("_handle_shapes");
      if (dtype_attr && shape_attr) {
        if (dtype_attr->list().type().empty()) {
          return errors::InvalidArgument(

      // Here TArgs types do not include types of the first two parameters,
      // i.e. the convolution input and the filter. TArgs are parameters for
      // the extras like the bias etc.
      auto attr = TypeAttr::get(getElementTypeOrSelf(contraction.getType()));
      SmallVector<Attribute, 4> targs_values(operands.size() - 2, attr);

      ArrayAttr targs_attr = ArrayAttr::get(context, targs_values);
      attrs.push_back(