%0 = "tf.BatchMatMulV2"(%arg0, %arg3) {adj_x = false,...

        auto& list = attr->list();
        if (list.shape_size() > idx) {
          llvm::SmallVector<int64_t, 4> shape;
          const TensorShapeProto& shape_proto = list.shape()[idx];
          if (shape_proto.unknown_rank())
            return mlir::UnrankedTensorType::get(element_type);
          TF_RETURN_IF_ERROR(ConvertToMlirShape(shape_proto, &shape));
          return GetTypeFromTFTensorShape(shape, element_type);
        }

    SINGLE_CASE(kF, TF_ATTR_FLOAT, -1);
    SINGLE_CASE(kB, TF_ATTR_BOOL, -1);
    SINGLE_CASE(kType, TF_ATTR_TYPE, -1);
    SINGLE_CASE(kShape, TF_ATTR_SHAPE,
                attr->shape().unknown_rank() ? -1 : attr->shape().dim_size());
    SINGLE_CASE(kTensor, TF_ATTR_TENSOR, -1);
#undef SINGLE_CASE

    case tensorflow::AttrValue::kList:
      metadata.is_list = 1;
      metadata.list_size = 0;

    // CHECK:      metadata
    // CHECK-SAME: shape {\0A unknown_rank: true

    func.return %0: tensor<*xi32>
  }
  func.func @_func(%arg0: tensor<*xi32>) -> tensor<*xi32> {
    func.return %arg0 : tensor<*xi32>
  }
}

// -----