s.add("valueStrength", Ascii.toLowerCase(valueStrength.toString()));
    }
    if (keyEquivalence != null) {
      s.addValue("keyEquivalence");
    }
    if (valueEquivalence != null) {
      s.addValue("valueEquivalence");
    }
    if (removalListener != null) {
      s.addValue("removalListener");
    }
    return s.toString();
  }

  /**

    }
    if (valueStrength != null) {
      s.add("valueStrength", Ascii.toLowerCase(valueStrength.toString()));
    }
    if (keyEquivalence != null) {
      s.addValue("keyEquivalence");
    }
    return s.toString();
  }

      s.add("valueStrength", Ascii.toLowerCase(valueStrength.toString()));
    }
    if (keyEquivalence != null) {
      s.addValue("keyEquivalence");
    }
    if (valueEquivalence != null) {
      s.addValue("valueEquivalence");
    }
    if (removalListener != null) {
      s.addValue("removalListener");
    }
    return s.toString();
  }

  /**

    String closingFutureToString() {
      // TODO(dpb): Better toString, in the style of Futures.transform etc.
      return toStringHelper("ClosingFuture")
          .add("status", status.get())
          .addValue(future)
          .toString();
    }
  }

  // TODO(dpb): Should we use a pair of ArrayLists instead of an IdentityHashMap?

  for (int64_t i = 0; i < input.NumElements(); ++i) {
    tensorflow::Example example;
    auto* feature_map = example.mutable_features()->mutable_feature();
    (*feature_map)["x"].mutable_float_list()->add_value(i);
    input.flat<tstring>()(i) = example.SerializeAsString();
  }

  const std::string input_op_name(
      tensorflow::ParseTensorName(input_name).first);
  TF_Operation* input_op =