class_docstr: Create a tfidf matrix from collection of documents of size [n_documents X n_features(hash_size)].
  
  Args:
      tokenizer: a tokenizer class
      hash_size: a hash size, power of two
      doc_index: a dictionary of document ids and their titles
      save_path: a path to **.npz** file where tfidf matrix is saved
      load_path: a path to **.npz** file where tfidf matrix is loaded from
  
  Attributes:
      hash_size: a hash size
      tokenizer: instance of a tokenizer class
      term_freqs: a dictionary with tfidf terms and their frequences
      doc_index: provided by a user ids or generated automatically ids
      rows: tfidf matrix rows corresponding to terms
      cols: tfidf matrix cols corresponding to docs
      data: tfidf matrix data corresponding to tfidf values
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docstr: Partially fit on one batch.
  
  Args:
      docs: a list of input documents
      doc_ids: a list of document ids corresponding to input documents
      doc_nums: a list of document integer ids as they appear in a database
  
  Returns:
      None
docstr_tok: ["Partially","fit","on","one","batch",".","Args",":","docs",":","a","list","of","input","documents","doc_ids",":","a","list","of","document","ids","corresponding","to","input","documents","doc_nums",":","a","list","of","document","integer","ids","as","they","appear","in","a","database","Returns",":","None"]
code: def partial_fit(
      self, docs: List[str], doc_ids: List[Any], doc_nums: List[int]
  ) -> None:
      """Partially fit on one batch.
  
      Args:
          docs: a list of input documents
          doc_ids: a list of document ids corresponding to input documents
          doc_nums: a list of document integer ids as they appear in a database
  
      Returns:
          None
  
      """
      for doc_id, i in zip(doc_ids, doc_nums):
          self.doc_index[doc_id] = i
  
      for batch_rows, batch_data, batch_cols in self.get_counts(
          docs, doc_ids
      ):
          self.rows.extend(batch_rows)
          self.cols.extend(batch_cols)
          self.data.extend(batch_data)
  
imports: from collections import Counter
  from logging import getLogger
  from typing import List, Any, Generator, Tuple, KeysView, ValuesView, Dict, Optional
  import numpy
  import scipy
  from scipy import sparse
  from sklearn.utils import murmurhash3_32
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.models.component import Component
  from deeppavlov.core.models.estimator import Estimator
label_desc: Use image node_id 10 for calling the HashingTfIdfVectorizer obj's underlying member method code with example usage: obj.partial_fit(docs, doc_ids, doc_nums) without return types