class_docstr: DALI base iterator class. Shouldn't be used directly.
  
  Parameters
  ----------
  pipelines : list of nvidia.dali.Pipeline
              List of pipelines to use
  output_map : list of (str, str)
               List of pairs (output_name, tag) which maps consecutive
               outputs of DALI pipelines to proper field in MXNet's
               DataBatch.
               tag is one of DALIGenericIterator.DATA_TAG
               and DALIGenericIterator.LABEL_TAG mapping given output
               for data or label correspondingly.
               output_names should be distinct.
  size : int, default = -1
              Number of samples in the shard for the wrapped pipeline (if there is more than one
              it is a sum). Providing -1 means that the iterator will work until StopIteration
              is raised from the inside of iter_setup(). The options `last_batch_policy`,
              `last_batch_padded` and `auto_reset` don't work in such case. It works with only
              one pipeline inside the iterator.
              Mutually exclusive with `reader_name` argument
  reader_name : str, default = None
              Name of the reader which will be queried for the shard size, number of shards, and
              all other properties necessary to count properly the number of relevant and padded
              samples that iterator needs to deal with. Sets `last_batch_padded`
              accordingly to the reader's configuration (`pad_last_batch` reader argument)
  auto_reset : string or bool, optional, default = False
              Whether the iterator resets itself for the next epoch or it requires reset() to be
              called explicitly.
  
              It can be one of the following values:
  
              * ``"no"``, ``False`` or ``None`` - at the end of epoch StopIteration is raised and
                reset() needs to be called. Calling ``iter()`` on the iterator would reset
                it as well.
              * ``"yes"`` or ``"True"``- at the end of epoch StopIteration is raised but reset()
                is called internally automatically
  
  fill_last_batch : bool, optional, default = None
              **Deprecated** Please use ``last_batch_policy`` instead
  
              Whether to fill the last batch with data up to 'self.batch_size'.
              The iterator would return the first integer multiple
              of self._num_gpus * self.batch_size entries which exceeds 'size'.
              Setting this flag to False will cause the iterator to return
              exactly 'size' entries.
  last_batch_policy: optional, default = LastBatchPolicy.FILL
              What to do with the last batch when there are not enough samples in the epoch
              to fully fill it. See :meth:`nvidia.dali.plugin.base_iterator.LastBatchPolicy`
  last_batch_padded : bool, optional, default = False
              Whether the last batch provided by DALI is padded with the last sample
              or it just wraps up. In the conjunction with ``last_batch_policy`` it tells
              if the iterator returning last batch with data only partially filled with
              data from the current epoch is dropping padding samples or samples from
              the next epoch. If set to False next
              epoch will end sooner as data from it was consumed but dropped. If set to
              True next epoch would be the same length as the first one. For this to happen,
              the option `pad_last_batch` in the reader needs to be set to True as well.
              It is overwritten when `reader_name` argument is provided
  prepare_first_batch : bool, optional, default = True
              Whether DALI should buffer the first batch right after the creation of the iterator,
              so one batch is already prepared when the iterator is prompted for the data
  
  Example
  -------
  With the data set ``[1,2,3,4,5,6,7]`` and the batch size 2:
  
  last_batch_policy = LastBatchPolicy.PARTIAL, last_batch_padded = True  -> last batch = ``[7]``,
  next iteration will return ``[1, 2]``
  
  last_batch_policy = LastBatchPolicy.PARTIAL, last_batch_padded = False -> last batch = ``[7]``,
  next iteration will return ``[2, 3]``
  
  last_batch_policy = LastBatchPolicy.FILL, last_batch_padded = True   -> last batch = ``[7, 7]``,
   next iteration will return ``[1, 2]``
  
  last_batch_policy = LastBatchPolicy.FILL, last_batch_padded = False  -> last batch = ``[7, 1]``,
  next iteration will return ``[2, 3]``
  
  last_batch_policy = LastBatchPolicy.DROP, last_batch_padded = True   -> last batch = ``[5, 6]``,
  next iteration will return ``[1, 2]``
  
  last_batch_policy = LastBatchPolicy.DROP, last_batch_padded = False  -> last batch = ``[5, 6]``,
  next iteration will return ``[2, 3]``
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code: def __len__(self):
      if self._reader_name:
          if self._last_batch_policy != LastBatchPolicy.DROP:
              return math.ceil(self.size / self.batch_size)
          else:
              return self.size // self.batch_size
      else:
          if self._last_batch_policy != LastBatchPolicy.DROP:
              return math.ceil(
                  self.size / (self._num_gpus * self.batch_size)
              )
          else:
              return self.size // (self._num_gpus * self.batch_size)
  
imports: from nvidia.dali import types
  import math
  import logging
  import numpy
  import warnings
  from enum import Enum, unique
  from collections.abc import Iterable
label_desc: Use image node_id 16 for calling the _DaliBaseIterator obj's underlying member method code with example usage: obj.__len__() and returns: math, unknown, math, unknown