removed unused values in samplers

"""Batch samplers that work with either random or sequential data samplers."""

import numpy as np

import torch

from torch.utils import data

    def __iter__(self):

        batch = []

        last_batch = None

        i = 0

        for idx in self.data_iterator(self.sampler, wrap_around=False):

            batch.append(idx)

                    yield tbatch

                    self.start_iter = 0

                i += 1

                last_batch = np.array(list(tbatch))

                batch = []

        batch_len = len(batch)

        if batch_len > 0 and not self.drop_last:

                self.sampler.wrap_around -= (self.batch_size)

                self.wrap_around += (len(batch))

                self.wrap_around %= self.batch_size

                if isinstance(self.sampler, TransposedSampler):

                    for i, idx in enumerate(self.data_iterator(

                            self.sampler, wrap_around=True)):

                        if i == 0:

                            continue

                        batch.append(idx)

                        new_batch_len = len(batch)

                        if len(batch) == self.batch_size:

                            break

            yield self._batch(batch)

        if self.wrap_last:

            self.sampler.wrap_around += self.batch_size