code: def transform(self, raw_X):
      msg = "'X' should be a 1-dimensional array with length 'num_samples'."
  
      if not dask.is_dask_collection(raw_X):
          return self._hasher(**self.get_params()).transform(raw_X)
  
      if isinstance(raw_X, db.Bag):
          bag2 = raw_X.map_partitions(self._transformer)
          objs = bag2.to_delayed()
          arrs = [
              da.from_delayed(
                  obj, (np.nan, self.n_features), self.dtype
              )
              for obj in objs
          ]
          result = da.concatenate(arrs, axis=0)
      elif isinstance(raw_X, dd.Series):
          result = raw_X.map_partitions(self._transformer)
      elif isinstance(raw_X, da.Array):
          # dask.Array
          chunks = ((np.nan,) * raw_X.numblocks[0], (self.n_features,))
          if raw_X.ndim == 1:
              result = raw_X.map_blocks(
                  self._transformer,
                  dtype="f8",
                  chunks=chunks,
                  new_axis=1,
              )
          else:
              raise ValueError(msg)
      else:
          raise ValueError(msg)
  
      meta = scipy.sparse.eye(0, format="csr")
      result._meta = meta
      return result
  
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 3 for calling the _BaseHasher obj's underlying member method code with example usage: obj.transform(raw_X) and returns: result, self