}
  return true;
}

bool QuantizationDriver::SetConstantResultParams(Operation* op) {
  DenseFPElementsAttr attr;
  const Value result = op->getResult(0);
  if (!matchPattern(result, m_Constant(&attr))) {
    return false;
  }
  // TODO: b/323478683 - Make storage_type_width and narrow_range configurable.
  Type final_type;
  const auto it = optimized_weights_.find(op);

				},
			},
			Headers: []*envoyroute.HeaderMatcher{
				{
					Name: "foo",
					HeaderMatchSpecifier: &envoyroute.HeaderMatcher_StringMatch{
						StringMatch: &matcher.StringMatcher{MatchPattern: &matcher.StringMatcher_Exact{Exact: "bar"}},
					},
					InvertMatch: false,
				},
			},
		}},
	}

	wantSecond := []*envoyroute.Route{

 private:
  LogicalResult matchAndRewrite(quantfork::QuantizeCastOp q_op,
                                PatternRewriter& rewriter) const override {
    DenseFPElementsAttr attr;
    if (!matchPattern(q_op.getArg(), m_Constant(&attr))) {
      return failure();
    }

    ShapedType tensor_qtype =
        mlir::cast<ShapedType>(q_op.getResult().getType());

  }
  if (op_name == "tfl.reshape") {
    // Flattens reshape ops when more than one dimension shape operand is given.
    mlir::DenseIntElementsAttr shape_attr;
    if (matchPattern(op_state.operands[1], m_Constant(&shape_attr))) {
      auto shape_ty =
          op_state.operands[1].getType().dyn_cast<RankedTensorType>();
      if (shape_ty != nullptr && shape_ty.hasRank() && shape_ty.getRank() > 1) {

			pkg.Incomplete = true
		}
	}

	matchers := make([]func(string) bool, len(patterns))
	for i, p := range patterns {
		if strings.Contains(p, "...") {
			matchers[i] = pkgpattern.MatchPattern(p)
		}
	}
	return pkgs, nil
}

// EnsureImport ensures that package p imports the named package.
func EnsureImport(p *Package, pkg string) {
	for _, d := range p.Internal.Imports {