buf.WriteByte('.')
		buf.WriteString(x.Sel.Name)

	case *ast.IndexExpr, *ast.IndexListExpr:
		ix := typeparams.UnpackIndexExpr(x)
		WriteExpr(buf, ix.X)
		buf.WriteByte('[')
		writeExprList(buf, ix.Indices)
		buf.WriteByte(']')

	case *ast.SliceExpr:
		WriteExpr(buf, x.X)
		buf.WriteByte('[')
		if x.Low != nil {
			WriteExpr(buf, x.Low)
		}
		buf.WriteByte(':')
		if x.High != nil {

          it.sourceCodePoint1 < codePoint -> -1
          it.sourceCodePoint0 > codePoint -> 1
          else -> 0
        }
      }

    // Code points must be in 0..0x10ffff.
    require(index in mappings.indices) { "unexpected code point: $codePoint" }

    val mapping = mappings[index]
    var result = true

    when (mapping.type) {
      TYPE_IGNORED -> Unit
      TYPE_MAPPED, TYPE_DISALLOWED_STD3_MAPPED -> {

// Returns whether the sharding is replicated. It includes sharding with
// REPLICATED type and replicated OTHER type.
bool IsReplicatedSharding(const xla::OpSharding& sharding);

// Returns a map of dimension indices and number of splits for tiled sharding.
absl::StatusOr<std::map<int, int>> GetDimensionIndicesAndNumSplitsFromSharding(
    const xla::OpSharding& sharding);
}  // namespace tensorflow

    public static void afterClass() throws Exception {
        runner.close();
        runner.clean();
    }

    @Before
    public void before() throws Exception {
        try {
            runner.admin().indices().prepareDelete("_all").execute().actionGet();
        } catch (IndexNotFoundException ignore) {

        }
        runner.refresh();
        suggester = Suggester.builder().build(runner.client(), "SuggesterTest");

      24, 26, 23, 26, 27, 26, 26, 27, 27, 27, 27, 27, 28, 27, 27, 27, 27, 27, 26,
    )

  private val root = Node()

  init {
    for (i in CODE_BIT_COUNTS.indices) {
      addCode(i, CODES[i], CODE_BIT_COUNTS[i].toInt())
    }
  }

  @Throws(IOException::class)
  fun encode(
    source: ByteString,
    sink: BufferedSink,
  ) {
    var accumulator = 0L

    for (const auto& phase : group.second) {
      for (const auto& pass : phase.second) {
        // Iterate over the pass_names_ and insert the pass pointer at all the
        // corresponding indices in the pass_ids vector.
        auto iter = pass_names.begin();
        while ((iter = std::find(iter, pass_names.end(), pass->name())) !=
               pass_names.end()) {

// file name incorporates the meta-data hash), and add the counter
// data file to the appropriate pod.
//
// This process is complicated by the fact that we need to keep track
// of directory indices for counter data files. Here is an example to
// motivate:
//
//	directory 1:
//
// M1   covmeta.9bbf1777f47b3fcacb05c38b035512d6
// C1   covcounters.9bbf1777f47b3fcacb05c38b035512d6.1677673.1662138360208416486

    - Unlike to the old quantizer, per-channel quantization is supported for
    weight-only TransposeConvOp.
  }];

  let methods = [
    InterfaceMethod<
      [{Returns the quantizable operand indices of the op.}],
      "std::vector<int>", "GetQuantizableOperandIndices",
      (ins), [{}], [{return {};}]>,
    InterfaceMethod<
      [{Returns whether the op has the kernel support for dynamic range

	case *ParenExpr:
		Walk(v, n.X)

	case *SelectorExpr:
		Walk(v, n.X)
		Walk(v, n.Sel)

	case *IndexExpr:
		Walk(v, n.X)
		Walk(v, n.Index)

	case *IndexListExpr:
		Walk(v, n.X)
		walkList(v, n.Indices)

	case *SliceExpr:
		Walk(v, n.X)
		if n.Low != nil {
			Walk(v, n.Low)
		}
		if n.High != nil {
			Walk(v, n.High)
		}
		if n.Max != nil {
			Walk(v, n.Max)
		}

		schema:       schema,
		root:         rootSchema,
		path:         root,
		knownFormats: formats,
		options:      options,
	})
}

// SubIndexValidator overrides the standard validator constructor for sub-indicies by
// returning our special ratcheting variant.
//
// If we can correlate an old value, we return a ratcheting validator to
// use for the child.
//