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 get_kwargs(transform, **kwargs):
      if isinstance(transform, BaaLTransform):
          t_kwargs = {k: kwargs[k] for k in transform.get_requires()}
      else:
          t_kwargs = {}
      return t_kwargs
  
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 5 for calling the FileDataset obj's underlying member method code with example usage: obj.get_kwargs(transform) and returns: t_kwargs