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"]
docstr: Transform a sequence of documents to a document-term matrix
  
  Args:
      X (iterable over raw text documents, length = n_samples)
          Samples. Each sample must be a text document (either bytes or
          unicode strings, file name or file object depending on the
          constructor argument) which will be tokenized and hashed.
  
  Returns:
      X (scipy.sparse matrix, shape = (n_samples, self.n_features))
          Document-term matrix.
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code: def transform(self, X):
      """Transform a sequence of documents to a document-term matrix
  
      Args:
          X (iterable over raw text documents, length = n_samples)
              Samples. Each sample must be a text document (either bytes or
              unicode strings, file name or file object depending on the
              constructor argument) which will be tokenized and hashed.
  
      Returns:
          X (scipy.sparse matrix, shape = (n_samples, self.n_features))
              Document-term matrix.
      """
      if (len(X) < self.chunksize) or (self.chunksize is None):
          return self._transform(X)
      else:
          return sparse.vstack(
              [self._transform(x) for x in self._chunks(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 2 for calling the HashingVectorizerChunked obj's underlying member method code with example usage: obj.transform(X) and returns: self, sparse