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.";

      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(

            res_types, res_attrs)))
      return failure();

    // Update the function type which has excluded the control args.
    func->setAttr("function_type", TypeAttr::get(rewriter.getFunctionType(
                                       arg_types, res_types)));

    // Update arg/result attributes.
    func.setAllArgAttrs(arg_attrs);
    func.setAllResultAttrs(res_attrs);

        auto min_max = GetMinMaxValuesForArgument(func_name, i);
        // The input min/max or mean/std are not specified, then skip.
        if (!min_max.first.has_value() || !min_max.second.has_value()) return;

        TypeAttr params = quant::GetQuantizedTypeAttr(
            builder, input_type, builder.getF64FloatAttr(min_max.first.value()),
            builder.getF64FloatAttr(min_max.second.value()),

        auto min_max = GetMinMaxValuesForArgument(func_name, i);
        // The input min/max or mean/std are not specified, then skip.
        if (!min_max.first.has_value() || !min_max.second.has_value()) return;

        TypeAttr params = quant::GetQuantizedTypeAttr(
            builder, input_type, builder.getF64FloatAttr(min_max.first.value()),
            builder.getF64FloatAttr(min_max.second.value()),

      return false;
    }
  }
  return true;
}

// Returns the tensor type created from the `shape_attr` and `type_attr`
// attributes.
Type GetType(Attribute shape_attr, Attribute type_attr) {
  auto shape = mlir::cast<tf_type::ShapeAttr>(shape_attr);
  auto type = mlir::cast<TypeAttr>(type_attr);
  if (shape.hasRank())
    return tensorflow::GetTypeFromTFTensorShape(shape.getShape(),