code: def build_array(bag, n_features, meta):
      name = "from-bag-" + bag.name
      layer = {(name, i, 0): (k, i) for k, i in bag.__dask_keys__()}
      dsk = dask.highlevelgraph.HighLevelGraph.from_collections(
          name, layer, dependencies=[bag]
      )
      chunks = ((np.nan,) * bag.npartitions, (n_features,))
      return da.Array(dsk, name, chunks, meta=meta)
  
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 1 for calling a global function with example usage: build_array(bag, n_features, meta) and returns: da