old_contents: from typing import Union, Iterable, Dict, Any
  from pathlib import Path
  import warnings
  import sys
  
  warnings.filterwarnings("ignore", message="numpy.dtype size changed")  # noqa
  warnings.filterwarnings("ignore", message="numpy.ufunc size changed")  # noqa
  
  # These are imported as part of the API
  from thinc.api import prefer_gpu, require_gpu  # noqa: F401
  from thinc.api import Config
  
  from . import pipeline  # noqa: F401
  from .cli.info import info  # noqa: F401
  from .glossary import explain  # noqa: F401
  from .about import __version__  # noqa: F401
  from .util import registry, logger  # noqa: F401
  
  from .errors import Errors
  from .language import Language
  from . import util
  
  
  if sys.maxunicode == 65535:
      raise SystemError(Errors.E130)
  
  
  def load(
      name: Union[str, Path],
      disable: Iterable[str] = util.SimpleFrozenList(),
      exclude: Iterable[str] = util.SimpleFrozenList(),
      config: Union[Dict[str, Any], Config] = util.SimpleFrozenDict(),
  ) -> Language:
      """Load a spaCy model from an installed package or a local path.
  
      name (str): Package name or model path.
      disable (Iterable[str]): Names of pipeline components to disable. Disabled
          pipes will be loaded but they won't be run unless you explicitly
          enable them by calling nlp.enable_pipe.
      exclude (Iterable[str]): Names of pipeline components to exclude. Excluded
          components won't be loaded.
      config (Dict[str, Any] / Config): Config overrides as nested dict or dict
          keyed by section values in dot notation.
      RETURNS (Language): The loaded nlp object.
      """
      return util.load_model(name, disable=disable, exclude=exclude, config=config)
  
  
  def blank(
      name: str,
      *,
      config: Union[Dict[str, Any], Config] = util.SimpleFrozenDict(),
      meta: Dict[str, Any] = util.SimpleFrozenDict()
  ) -> Language:
      """Create a blank nlp object for a given language code.
  
      name (str): The language code, e.g. "en".
      config (Dict[str, Any] / Config): Optional config overrides.
      meta (Dict[str, Any]): Overrides for nlp.meta.
      RETURNS (Language): The nlp object.
      """
      LangClass = util.get_lang_class(name)
      # We should accept both dot notation and nested dict here for consistency
      config = util.dot_to_dict(config)
      return LangClass.from_config(config, meta=meta)
  
new_contents: from typing import Union, Iterable, Dict, Any
  from pathlib import Path
  import warnings
  import sys
  
  warnings.filterwarnings("ignore", message="numpy.dtype size changed")  # noqa
  warnings.filterwarnings("ignore", message="numpy.ufunc size changed")  # noqa
  
  # These are imported as part of the API
  from thinc.api import prefer_gpu, require_gpu  # noqa: F401
  from thinc.api import Config
  
  from . import pipeline  # noqa: F401
  from .cli.info import info  # noqa: F401
  from .glossary import explain  # noqa: F401
  from .about import __version__  # noqa: F401
  from .util import registry, logger  # noqa: F401
  
  from .errors import Errors
  from .language import Language
  from .vocab import Vocab
  from . import util
  
  
  if sys.maxunicode == 65535:
      raise SystemError(Errors.E130)
  
  
  def load(
      name: Union[str, Path],
      disable: Iterable[str] = util.SimpleFrozenList(),
      exclude: Iterable[str] = util.SimpleFrozenList(),
      config: Union[Dict[str, Any], Config] = util.SimpleFrozenDict(),
  ) -> Language:
      """Load a spaCy model from an installed package or a local path.
  
      name (str): Package name or model path.
      disable (Iterable[str]): Names of pipeline components to disable. Disabled
          pipes will be loaded but they won't be run unless you explicitly
          enable them by calling nlp.enable_pipe.
      exclude (Iterable[str]): Names of pipeline components to exclude. Excluded
          components won't be loaded.
      config (Dict[str, Any] / Config): Config overrides as nested dict or dict
          keyed by section values in dot notation.
      RETURNS (Language): The loaded nlp object.
      """
      return util.load_model(name, disable=disable, exclude=exclude, config=config)
  
  
  def blank(
      name: str,
      *,
      vocab: Union[Vocab, bool] = True,
      config: Union[Dict[str, Any], Config] = util.SimpleFrozenDict(),
      meta: Dict[str, Any] = util.SimpleFrozenDict()
  ) -> Language:
      """Create a blank nlp object for a given language code.
  
      name (str): The language code, e.g. "en".
      vocab (Vocab): A Vocab object. If True, a vocab is created.
      config (Dict[str, Any] / Config): Optional config overrides.
      meta (Dict[str, Any]): Overrides for nlp.meta.
      RETURNS (Language): The nlp object.
      """
      LangClass = util.get_lang_class(name)
      # We should accept both dot notation and nested dict here for consistency
      config = util.dot_to_dict(config)
      return LangClass.from_config(config, meta=meta)
  
subject: Support vocab arg in spacy.blank