var first = true
        list.forEach {
            if (first) first = false else comma()
            body(it)
        }
        endArray()
    }

    private
    fun beginArray() {
        write('[')
    }

    private
    fun endArray() {
        write(']')
    }

    private
    fun property(name: String, value: String) {
        property(name) { jsonString(value) }

            out.name("subcategories").beginArray();
            for (String sc : value.getSubcategories()) {
                out.value(sc);
            }
            out.endArray();
            out.endObject();
        }

        @Override
        public ProblemCategory read(JsonReader in) throws IOException {
            in.beginObject();
            String namespace = null;

    private List<Object> readArray(JsonReader reader) {
        List<Object> values = []
        reader.beginArray()
        while (reader.peek() != JsonToken.END_ARRAY) {
            Object value = readAny(reader)
            values.add(value)
        }
        reader.endArray()
        return values
    }

    static Map<String, String> normalizeForTests(Map<String, ?> attributes) {

    [`tf.experimental.numpy`](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy),
    which is a NumPy-compatible API for writing TF programs. This module
    provides class `ndarray`, which mimics the `ndarray` class in NumPy, and
    wraps an immutable `tf.Tensor` under the hood. A subset of NumPy functions
    (e.g. `numpy.add`) are provided. Their inter-operation with TF facilities is
    seamless in most cases. See