class_docstr: Lazy loading dataset for processing PDF documents.
  
  This dataset allows lazy loading of PDF documents and provides access to processed images
  using a specified preparation function.
  
  Args:
      pdf (str): Path to the PDF document.
      prepare (Callable): A preparation function to process the images.
  
  Attributes:
      name (str): Name of the PDF document.
code: def ignore_none_collate(batch):
      if batch is None:
          return None, None
      try:
          _batch = []
          for i, x in enumerate(batch):
              image, name = x
              if image is not None:
                  _batch.append(x)
              elif name:
                  if i > 0:
                      _batch[-1] = (_batch[-1][0], name)
                  elif len(batch) > 1:
                      _batch.append((batch[1][0] * 0, name))
          if len(_batch) == 0:
              return None, None
          return torch.utils.data.dataloader.default_collate(_batch)
      except AttributeError:
          pass
      return None, None
  
imports: import logging
  import os
  from math import prod
  from pathlib import Path
  from functools import partial
  import random
  from typing import Dict, Tuple, Callable
  from PIL import Image, UnidentifiedImageError
  from typing import List, Optional
  import torch
  import pypdf
  import orjson
  from torch.utils.data import Dataset
  from transformers.modeling_utils import PreTrainedModel
  from nougat.dataset.rasterize import rasterize_paper
label_desc: Use image node_id 4 for calling the LazyDataset obj's underlying member method code with example usage: obj.ignore_none_collate(batch) and returns: None,  None, None,  None, torch, None,  None