code: def MultiCategoryBlock(
      encoded: bool = False,  # Whether the data comes in one-hot encoded
      vocab: MutableSequence
      | pd.Series = None,  # List of unique class names
      add_na: bool = False,  # Add `#na#` to `vocab`
  ):
      "`TransformBlock` for multi-label categorical targets"
      tfm = (
          EncodedMultiCategorize(vocab=vocab)
          if encoded
          else [
              MultiCategorize(vocab=vocab, add_na=add_na),
              OneHotEncode,
          ]
      )
      return TransformBlock(type_tfms=tfm)
  
imports: from __future__ import annotations
  from ..torch_basics import *
  from .core import *
  from .load import *
  from .external import *
  from .transforms import *
  from inspect import isfunction, ismethod
  from .load import _collate_types
label_desc: Use image node_id 2 for calling a global function with example usage: MultiCategoryBlock(encoded, vocab, add_na) and returns: TransformBlock