code: def vocabulary_length(vocabulary):
      if isinstance(vocabulary, dict):
          return len(vocabulary)
      elif isinstance(vocabulary, Delayed):
          try:
              return len(vocabulary)
          except TypeError:
              return len(vocabulary.compute())
      elif isinstance(vocabulary, distributed.Future):
          client = get_client()
          future = client.submit(len, vocabulary)
          wait(future)
          result = future.result()
          return result
      else:
          raise ValueError(
              f"Unknown vocabulary type {type(vocabulary)}."
          )
  
imports: import itertools
  import dask
  import dask.array
  import dask.bag
  import dask.dataframe
  import distributed
  import numpy
  import scipy.sparse
  import sklearn.base
  import sklearn.feature_extraction.text
  from dask.delayed import Delayed
  from distributed import get_client, wait
  from sklearn.utils.validation import check_is_fitted
label_desc: Use image node_id 2 for calling a global function with example usage: vocabulary_length(vocabulary) and returns: len, len, result, len