auto callsite_loc3 = mlir::CallSiteLoc::get(loc_filtered2, loc3);

  // Test with filter on.
  StatusScopedDiagnosticHandler ssdh_filter(&context, false, true);
  emitError(callsite_loc) << "Error 1";
  emitError(callsite_loc2) << "Error 2";
  emitError(callsite_loc3) << "Error 3";
  Status s_filtered = ssdh_filter.ConsumeStatus();
  // Check for the files that should not be filtered.

    if (!backendConfig)
      return op->emitError(
          "custom_call has no 'composite.backend_config' attribute or the "
          "attribute is not a dictionary");

    auto name = mlir::dyn_cast<StringAttr>(backendConfig.get("name"));
    if (!name)
      return op->emitError(
          "backend_config has no 'name' key or the name value is not a string");

    auto attrs =

  absl::flat_hash_set<Node*> control_ret_nodes;
  Status status = tensorflow::tf2xla::v2::ConvertMlirToGraph(
      module_in, confs, &graph, &flib_def, &control_ret_nodes);
  if (!status.ok()) {
    mlir::emitError(mlir::UnknownLoc::get(&ctx)) << status.message();
    return signalPassFailure();
  }

  // Run each of the passes that were selected.
  GraphConstructorOptions opts;
  opts.allow_internal_ops = true;

    absl::StatusOr<SmallVector<SmallVector<int64_t>>> parsed_shapes;
    if (!input_arg_shapes_.empty()) {
      parsed_shapes = ParseArgumentShapes(input_arg_shapes_);
      if (!parsed_shapes.ok()) {
        getOperation().emitError() << parsed_shapes.status().message();
        return signalPassFailure();
      }
      input_shapes_vec = SmallVector<ArrayRef<int64_t>>{parsed_shapes->begin(),

  if (!(tensor_type && tensor_type.hasRank())) {
    return op->emitError()
           << "cannot convert op with unranked or non-tensor input type "
           << tensor_type << ".";
  }

  int rank = tensor_type.getRank();
  if (rank <= partition_dim) {
    return op->emitError() << "cannot partition " << first_operand_type
                           << " (rank = " << rank << ") along dimension "

  auto result_type =
      OpTrait::util::getBroadcastedType(x.getType(), y.getType());
  if (!result_type) {
    if (incompatible_shape_error.getValue()) {
      mlir::emitError(loc, "non-broadcastable operands");
    } else {
      return UnrankedTensorType::get(builder->getI1Type());
    }
  }

  auto ranked_type = mlir::dyn_cast<RankedTensorType>(result_type);

    return func->emitError()
           << "'" << attr_name << "' attribute for " << kMaxUnpooling
           << " must be set and has size of " << N;
  }
  results->reserve(N);

  for (Attribute integer_attr : array_attr.getValue()) {
    IntegerAttr value = mlir::dyn_cast<IntegerAttr>(integer_attr);
    if (!value) {
      return func->emitError()

      // GuaranteeAllFuncsOneUsePass to remove "tf.entry_function" or
      // "tf_saved_model.initializer_type" attribute from the callee of the
      // inner calls if the problem ever arises.
      entry_func->emitError()
          << "TF2XLA MLIR Non-replicated Phase 1 Bridge expects no nested calls"
             " of entry functions as they prevent graph traversal in some "
             "passes from "
             "working correctly";

                                          Location loc) {
  auto dims_type = mlir::dyn_cast<RankedTensorType>(dims.getType());
  if (!dims_type) return success();
  if (dims_type.getRank() > 1)
    return emitError(loc, "dimensions can only be 0D or 1D tensor");

  auto input_type = mlir::dyn_cast<RankedTensorType>(input.getType());
  if (!input_type) return success();
  int64_t rank = input_type.getRank();

  DeviceNameUtils::ParsedName parsed_name;
  if (!DeviceNameUtils::ParseFullName(
          absl::string_view(device.data(), device.size()), &parsed_name))
    return mlir::emitError(loc) << "invalid device '" << device << "'";

  if (!parsed_name.has_id)
    return mlir::emitError(loc) << "device '" << device << "' has no id";

  *device_ordinal = parsed_name.id;
  return mlir::success();
}