class_docstr: A stats generator which counts the number of missing values in a path.
class_docstr_tok: ["A","stats","generator","which","counts","the","number","of","missing","values","in","a","path","."]
docstr: Accumulates the number of missing rows from new batch.
docstr_tok: ["Accumulates","the","number","of","missing","rows","from","new","batch","."]
code: def add_input(
      self, accumulator, batch: input_batch.InputBatch
  ) -> int:
      """Accumulates the number of missing rows from new batch."""
      null_mask = batch.null_mask(self._path)
      if self._required_paths:
          required_null_mask = batch.all_null_mask(
              *self._required_paths
          )
          null_mask = null_mask & ~required_null_mask
      return accumulator + np.sum(null_mask)
  
file_path: data-validation/tensorflow_data_validation/statistics/generators/constituents/count_missing_generator.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensorflow_data_validation import types
  from tensorflow_data_validation.statistics.generators import input_batch
  from tensorflow_data_validation.statistics.generators import stats_generator
  from typing import Iterable, Optional, Text, Tuple, Union
label_desc: Use image node_id 5 for calling the CountMissingGenerator obj's underlying member method code with example usage: obj.add_input(accumulator, batch) and returns: unknown