petastorm/pytorch.py [169:184]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        self._batch_acc = []
        self.shuffling_queue_capacity = shuffling_queue_capacity
        self._in_iter = None

    def _iter_impl(self):
        """
        The Data Loader iterator stops the for-loop when reader runs out of samples.
        """
        # As we iterate over incoming samples, we are going to store them in `self._batch_acc`, until we have a batch of
        # the requested batch_size ready.

        keys = None
        if self.shuffling_queue_capacity > 0:
            # We can not know what is the reasonable number to use for the extra capacity, so we set a huge number
            # and give up on the unbound growth protection mechanism.
            min_after_dequeue = self.shuffling_queue_capacity - 1
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



petastorm/pytorch.py [294:310]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        self._batch_acc = []
        self.shuffling_queue_capacity = shuffling_queue_capacity
        self._in_iter = None

    def _iter_impl(self):
        """
        The Data Loader iterator stops the for-loop when reader runs out of samples.
        """
        # As we iterate over incoming samples, we are going to store them in `self._batch_acc`, until we have a batch of
        # the requested batch_size ready.

        keys = None
        if self.shuffling_queue_capacity > 0:
            # We can not know what is the reasonable number to use for the extra capacity, so we set a huge number
            # and give up on the unbound growth protection mechanism.
            # To keep the same behavior as DataLoader, we need to increase the shuffling_queue_capacity
            min_after_dequeue = self.shuffling_queue_capacity - 1
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -