mean_op.getAxis(), mean_op.getKeepDims());

  // Insert a requant op.
  rewriter.replaceOpWithNewOp<TFL::QuantizeOp>(
      mean_op, output_type, new_mean_op, mlir::TypeAttr::get(output_type));
  return success();
}

}  // namespace tac
}  // namespace TFL

  int64_t out = 0;
  for (int64_t outer = 0; outer < outer_size; ++outer) {
    for (auto op : operands) {
      auto typed_attr = op.cast<TypedAttr>();
      const int64_t dim_size =
          typed_attr.getType().cast<RankedTensorType>().getDimSize(axis);
      const int64_t inner_size = dim_size * base_inner_size;

    // types. But we need to wrap it up with ElementsAttr to construct
    // valid TensorFlow constants.
    auto typed_attr = mlir::cast<TypedAttr>(value);
    type = tensorflow::GetTypeFromTFTensorShape(/*shape=*/{},
                                                typed_attr.getType());
    return ConstOp::build(builder, result, DenseElementsAttr::get(type, value));
  }

  return builder.getI32IntegerAttr(value);
}

static mlir::Attribute BuildTypeAttr(tflite::TensorType value,
                                     mlir::Builder builder) {
  return mlir::TypeAttr::get(ConvertElementType(value, builder));
}

static mlir::Attribute BuildTFL_AFAttr(tflite::ActivationFunctionType value,
                                       mlir::Builder builder) {

    type_attr = attr_value_pb2.AttrValue(type=types_pb2.DT_QINT8)
    if quantize:
      self.assertTrue(
          self._contains_op(output_graphdef, 'Const', 'dtype', type_attr)
      )
    else:
      self.assertFalse(
          self._contains_op(output_graphdef, 'Const', 'dtype', type_attr)
      )

  @parameterized.named_parameters(

    // Const op can have a result of dynamic shaped type (e.g. due to constant
    // folding), but we can still derive the shape of a constant tensor for
    // its attribute type.
    auto tensor_attr = mlir::cast<mlir::TypedAttr>(inst->getAttr("value"));
    llvm::ArrayRef<int64_t> shape_ref =
        mlir::cast<TensorType>(tensor_attr.getType()).getShape();
    if (mlir::failed(check_shape(shape_ref))) return std::nullopt;

    TypeAttr:$key_dtype,
    TypeAttr:$value_dtype
  );

  let results = (outs
    Res<TF_ResourceTensor, [{Handle to a table.}], [TF_LookupTableAlloc]>:$table_handle
  );

  let builders = [
    OpBuilder<(ins "StringAttr":$container, "StringAttr":$shared_name,
      "BoolAttr":$use_node_name_sharing, "TypeAttr":$key_dtype, "TypeAttr":$value_dtype),
    [{