Type resultTy = op->getOpResult(i).getType();
      if (QuantizedType::getQuantizedElementType(resultTy)) {
        replaceResults.push_back(rewriter.create<TFL::QuantizeOp>(
            loc, resultTy, result, TypeAttr::get(resultTy)));
        continue;
      }

      replaceResults.push_back(result);
    }

    rewriter.replaceOp(op, replaceResults);

    return success();
  }
};

          }
          attribute = TypeAttr::get(type);
        }
        Value attr_cst;
        // Wrap these special attributes as a special TFR constant, so the SSA
        // value has a valid type to be used as TFR function argument. These
        // attributes are not expected to be manipulated by the lowering passes.
        if (mlir::isa<TypeAttr>(attribute) || mlir::isa<ArrayAttr>(attribute) ||

        output_shape,
        mlir::cast<quant::UniformQuantizedType>(output_type.getElementType())
            .getStorageType());
    rewriter.replaceOpWithNewOp<QConstOp>(
        op, TypeAttr::get(result_type),
        DenseIntElementsAttr::get(values_type, new_values));
    return success();
  }
};

// Fold constant quantized Reshape ops.
struct FoldReshapeOp : public OpRewritePattern<ReshapeOp> {

        "effective_hidden_scale_intermediate"};
    for (auto type_and_name :
         llvm::zip(intermediate_types, kIntermediateNames)) {
      mlir::TypeAttr type_attr =
          mlir::TypeAttr::get(std::get<0>(type_and_name));
      auto named_attr =
          builder.getNamedAttr(std::get<1>(type_and_name), type_attr);
      op_state.addAttribute(named_attr.getName(), named_attr.getValue());

      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) {

    // determined while going through quantization passes.
    OptionalAttr<TypeAttr>:$input_to_input_intermediate,
    OptionalAttr<TypeAttr>:$input_to_forget_intermediate,
    OptionalAttr<TypeAttr>:$input_to_cell_intermediate,
    OptionalAttr<TypeAttr>:$input_to_output_intermediate,
    OptionalAttr<TypeAttr>:$effective_hidden_scale_intermediate
  );

  let results = (outs AnyTensor:$output);

  llvm::SmallVector<Attribute, 2> type_attrs;
  for (Type type : dataset_types) {
    shape_attrs.push_back(
        TF::ShapeAttr::get(builder.getContext(), mlir::cast<ShapedType>(type)));
    type_attrs.push_back(TypeAttr::get(getElementTypeOrSelf(type)));
  }

  auto anonymous_iterator = builder.create<AnonymousIteratorV3Op>(
      reduce_dataset.getLoc(),
      RankedTensorType::get({}, builder.getType<ResourceType>()),

      // 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(