errorFmtFunc = f
}

// uniq swaps away duplicate elements in data, returning the size of the
// unique set. data is expected to be pre-sorted, and the resulting set in
// the range [0:size] will remain in sorted order. Uniq, following a
// sort.Sort call, can be used to prepare arbitrary inputs for use as sets.
func uniq(data sort.Interface) (size int) {
	p, l := 0, data.Len()
	if l <= 1 {

      if (n == 0) {
        return;
      }
      Arrays.sort(elements, 0, n, comparator);
      int unique = 1;
      for (int i = 1; i < n; i++) {
        int cmp = comparator.compare(elements[unique - 1], elements[i]);
        if (cmp < 0) {
          elements[unique++] = elements[i];
        } else if (cmp > 0) {
          throw new AssertionError(

		if err != nil {
			t.Fatalf("failed to parse user unique, got error %v", err)
		}

		constraints := user.ParseUniqueConstraints()
		if len(constraints) != 1 {
			t.Fatalf("failed to find unique constraint, got %v", constraints)
		}

		for key := range constraints {
			if len(key) != 63 {
				t.Errorf("failed to find unique constraint, got %v", constraints)
			}
		}
	})

class OpDeleter {
 public:
  void operator()(TFE_Op* to_delete) const { TFE_DeleteOp(to_delete); }
};

using OpPtr = std::unique_ptr<TFE_Op, OpDeleter>;

class StatusDeleter {
 public:
  void operator()(TF_Status* to_delete) const { TF_DeleteStatus(to_delete); }
};

using StatusPtr = std::unique_ptr<TF_Status, StatusDeleter>;

class ExecutorDeleter {
 public:
  void operator()(TFE_Executor* to_delete) const {

// register/use filesystems.

namespace tensorflow {

using UniquePtrTo_TF_Status =
    ::std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)>;

Status ModularFileSystem::NewRandomAccessFile(
    const std::string& fname, TransactionToken* token,
    std::unique_ptr<RandomAccessFile>* result) {
  if (ops_->new_random_access_file == nullptr)
    return errors::Unimplemented(tensorflow::strings::StrCat(

  # Column 1 is left only, Column 2 is right only, Column 3 is shared lines
  # Select lines unique to actual_licenses, i.e. extra licenses.
  comm -1 -3 $BATS_TEST_TMPDIR/expected_licenses $BATS_TEST_TMPDIR/actual_licenses | grep -v -f $BATS_TEST_TMPDIR/allowed_to_be_extra > $BATS_TEST_TMPDIR/actual_extra_licenses || true
  # Select lines unique to expected_licenses, i.e. missing licenses

In this case you will have to ensure that each operation ID is **unique** in some other way.

For example, you could make sure that each *path operation* has a tag, and then generate the operation ID based on the **tag** and the *path operation* **name** (the function name).

### Custom Generate Unique ID Function

  server_def.set_task_index(1);
  std::unique_ptr<tensorflow::GrpcServer> worker_server1;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server1)
                  .ok());
  ASSERT_TRUE(worker_server1->Start().ok());

  server_def.set_task_index(2);
  std::unique_ptr<tensorflow::GrpcServer> worker_server2;

        throw new IllegalArgumentException(
            "overflowed computing offset(" + origin + ", " + distance + ")");
      }
    }
    return current;
  }

  /**
   * Returns the unique least value of type {@code C} that is greater than {@code value}, or {@code
   * null} if none exists. Inverse operation to {@link #previous}.
   *
   * @param value any value of type {@code C}

	unique, ok := columnType.Unique()
	if !ok || field.PrimaryKey {
		return nil // skip primary key
	}
	// By default, ColumnType's Unique is not affected by UniqueIndex, so we don't care about UniqueIndex.
	return m.RunWithValue(value, func(stmt *gorm.Statement) error {
		// We're currently only receiving boolean values on `Unique` tag,
		// so the UniqueConstraint name is fixed