class_docstr: The EntityRuler lets you add spans to the `Doc.ents` using token-based
  rules or exact phrase matches. It can be combined with the statistical
  `EntityRecognizer` to boost accuracy, or used on its own to implement a
  purely rule-based entity recognition system. After initialization, the
  component is typically added to the pipeline using `nlp.add_pipe`.
  
  DOCS: https://spacy.io/api/entityruler
  USAGE: https://spacy.io/usage/rule-based-matching#entityruler
docstr: Join Entity label with ent_id if the pattern has an `id` attribute
  If ent_id is not a string, the label is returned as is.
  
  label (str): The label to set for ent.label_
  ent_id (str): The label
  RETURNS (str): The ent_label joined with configured `ent_id_sep`
code: def _create_label(self, label: Any, ent_id: Any) -> str:
      """Join Entity label with ent_id if the pattern has an `id` attribute
      If ent_id is not a string, the label is returned as is.
  
      label (str): The label to set for ent.label_
      ent_id (str): The label
      RETURNS (str): The ent_label joined with configured `ent_id_sep`
      """
      if isinstance(ent_id, str):
          label = f"{label}{self.ent_id_sep}{ent_id}"
      return label
  
imports: import warnings
  from collections import defaultdict
  from pathlib import Path
  from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
  import srsly
  from ..errors import Errors, Warnings
  from ..language import Language
  from ..matcher import Matcher, PhraseMatcher
  from ..matcher.levenshtein import levenshtein_compare
  from ..scorer import get_ner_prf
  from ..tokens import Doc, Span
  from ..training import Example
  from ..util import SimpleFrozenList, ensure_path, from_disk, registry, to_disk
  from .pipe import Pipe
label_desc: Use image node_id 16 for calling the EntityRuler obj's underlying member method code with example usage: obj._create_label(label, ent_id) and returns: label