class_docstr: Equivalent to HashingVectorizer but with chunked prediction
  
  Args:
      chunksize (int): size of transform chunks
class_docstr_tok: ["Equivalent","to","HashingVectorizer","but","with","chunked","prediction","Args",":","chunksize","(","int",")",":","size","of","transform","chunks"]
code: def _transform(self, X):
      if isinstance(X, str):
          raise ValueError(
              "Iterable over raw text documents expected, string object received."
          )
  
      analyzer = self.build_analyzer()
      X = self._get_hasher().transform(analyzer(doc) for doc in X)
      if self.binary:
          X.data.fill(1)
      if self.norm is not None:
          X = normalize(X, norm=self.norm, copy=False)
      return X
  
imports: import warnings
  import pandas
  import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.preprocessing import MultiLabelBinarizer, FunctionTransformer, LabelEncoder, normalize
  from sklearn.feature_extraction.text import HashingVectorizer
  from sklearn import feature_selection
label_desc: Use image node_id 3 for calling the HashingVectorizerChunked obj's underlying member method code with example usage: obj._transform(X) and returns: X