}
      return result;
    }

    @Override
    public BaseEncoding ignoreCase() {
      BaseEncoding result = ignoreCase;
      if (result == null) {
        Alphabet ignore = alphabet.ignoreCase();
        result = ignoreCase = (ignore == alphabet) ? this : newInstance(ignore, paddingChar);
      }
      return result;
    }

        return emptyList()
      }

    var cookies: MutableList<Cookie>? = null
    for ((key, value) in cookieHeaders) {
      if (("Cookie".equals(key, ignoreCase = true) || "Cookie2".equals(key, ignoreCase = true)) &&
        value.isNotEmpty()
      ) {
        for (header in value) {
          if (cookies == null) cookies = mutableListOf()

     */
    private fun Headers.varyFields(): Set<String> {
      var result: MutableSet<String>? = null
      for (i in 0 until size) {
        if (!"Vary".equals(name(i), ignoreCase = true)) {
          continue
        }

        val value = value(i)
        if (result == null) {
          result = TreeSet(String.CASE_INSENSITIVE_ORDER)
        }

            `use_causal_mask=True` will compute a causal attention mask, and
            optionally combine it with any `attention_mask` passed in directly
            when calling the layer.
    *   Added `ignore_class` argument in the loss
        `SparseCategoricalCrossentropy` and metrics `IoU` and `MeanIoU`, to
        specify a class index to be ignored during loss/metric computation (e.g.
        a background/void class).