code: def __init__(
      self,
      dataset,
      sampler,
      batch_size=128,
      ignore_last=False,
      augmentation_pipeline=None,
  ):
      # store our dataset as well
      self.dataset = dataset
      self.sampler = sampler
      self.sample_it = iter(self.sampler)
  
      self.ignore_last = ignore_last
      self.batch_size = batch_size
      self.total_size = len(sampler)
      self.augmentation_pipeline = augmentation_pipeline
      self.steps_per_epoch = self._compute_steps_per_epoch()
      self.index = 0
      self.step = 0
  
file_path: ludwig/ludwig/data/batcher/random_access.py
imports: import logging
  import math
  import torch
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.data.batcher.base import Batcher
label_desc: Use image node_id 1 to create a new RandomAccessBatcher object from inherited base classes: Batcher with example: obj = RandomAccessBatcher(dataset, sampler, batch_size, ignore_last, augmentation_pipeline)