class_docstr: Dataset object that load the files and apply a transformation.
  
  Args:
      files (List[str]): The files.
      lbls (List[Any]): The labels, -1 indicates that the label is unknown.
      transform (Optional[Callable]): torchvision.transform pipeline.
      target_transform (Optional[Callable]): Function that modifies the target.
      image_load_fn (Optional[Callable]): Function that loads the image, by default uses PIL.
      seed (Optional[int]): Will set a seed before and between DA.
class_docstr_tok: ["Dataset","object","that","load","the","files","and","apply","a","transformation",".","Args",":","files","(","List","[","str","]",")",":","The","files",".","lbls","(","List","[","Any","]",")",":","The","labels",",","-1","indicates","that","the","label","is","unknown",".","transform","(","Optional","[","Callable","]",")",":","torchvision.transform","pipeline",".","target_transform","(","Optional","[","Callable","]",")",":","Function","that","modifies","the","target",".","image_load_fn","(","Optional","[","Callable","]",")",":","Function","that","loads","the","image",",","by","default","uses","PIL",".","seed","(","Optional","[","int","]",")",":","Will","set","a","seed","before","and","between","DA","."]
code: def __getitem__(self, idx):
      x, y = self.files[idx], self.lbls[idx]
  
      np.random.seed(self.seed)
      batch_seed = np.random.randint(0, 100, 1).item()
      seed_all(batch_seed + idx)
  
      img = self.image_load_fn(x)
      kwargs = self.get_kwargs(
          self.transform, image_shape=img.size, idx=idx
      )
  
      if self.transform:
          img_t = self.transform(img, **kwargs)
      else:
          img_t = img
  
      if self.target_transform:
          seed_all(batch_seed + idx)
          kwargs = self.get_kwargs(
              self.target_transform, image_shape=img.size, idx=idx
          )
          y = self.target_transform(y, **kwargs)
      return img_t, y
  
file_path: baal/baal/active/file_dataset.py
imports: import random
  import warnings
  from typing import Any, Callable, Optional, List, Sequence
  import numpy
  import torch
  from PIL import Image
  from torch.utils.data import Dataset
  from baal.utils.transforms import BaaLTransform
label_desc: Use image node_id 4 for calling the FileDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: img_t,  y