FLAGS[prefixFlag or frameFlag or FLAG_PADDED] =
          FLAGS[prefixFlag] + '|'.toString() + FLAGS[frameFlag] + "|PADDED"
      }
    }

    for (i in FLAGS.indices) { // Fill in holes with binary representation.
      if (FLAGS[i] == null) FLAGS[i] = BINARY[i]
    }
  }

  /**
   * Returns a human-readable representation of HTTP/2 frame headers.
   *
   * The format is:

                              additional_attributes.end()) {}

  // Name of the output kernel implementing the contraction fusion.
  std::string output_kernel;

  // Indices of additional arguments that will be forwarded to the fused
  // operation (e.g. forward bias vector if fusing BiasAdd operation).
  SmallVector<int, 4> additional_arguments;

  // Add additional attributes to the fused node.

  SmallVector<tf_device::ClusterFuncOp, 4> cluster_funcs;
  ModuleOp module = getOperation();
  module.walk([&](tf_device::ClusterFuncOp cluster_func) {
    // Map resource values to pair of input-output indices.
    llvm::DenseMap<Value, AliasInfo> resource_alias_info_map;
    if (failed(BuildAliasingInfo(cluster_func, resource_alias_info_map)) ||
        resource_alias_info_map.empty()) {
      return;
    }

		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

  const int num_regions = parallel_execute.getNumRegions();
  llvm::SmallVector<Value, 4> results_to_remap;

  // Go through each region and find AssignVariableOps that can be moved into
  // the parallel_execute region. Result indices by region index are collected,
  // so they can be removed afterwards.
  llvm::SmallVector<llvm::SmallVector<int, 4>, 4> results_to_remove_by_region;
  results_to_remove_by_region.resize(num_regions);

  Callable add(TFLIB_CALLABLE_ADAPTOR(AddIntegersTyped));
  auto res = add.Call<Integer>(Integer(3), Integer(1));
  EXPECT_EQ(res->get(), 4);
}

TEST(TypeUneraseCallTest, TestAppend) {
  // Append some indices to a list.
  Callable append(TFLIB_CALLABLE_ADAPTOR(AppendIntegerToList));
  List l;
  TF_ASSERT_OK(append.Call<None>(l, Integer(3)).status());
  TF_ASSERT_OK(append.Call<None>(l, Integer(6)).status());

	}
	return Pos{base, p.lico}
}

// FileTable returns a slice of all files used to build this package.
func (t *PosTable) FileTable() []string {
	// Create a LUT of the global package level file indices. This table is what
	// is written in the debug_lines header, the file[N] will be referenced as
	// N+1 in the debug_lines table.
	fileLUT := make([]string, len(t.nameMap))
	for str, i := range t.nameMap {

    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");