class_docstr: Basic wrapper around several `DataLoader`s with factory methods for computer vision problems
class_docstr_tok: ["Basic","wrapper","around","several","`","DataLoader","`","s","with","factory","methods","for","computer","vision","problems"]
docstr: Create from list of `fnames` in `path`s with `label_func`
docstr_tok: ["Create","from","list","of","`","fnames","`","in","`","path","`","s","with","`","label_func","`"]
code: def from_path_func(
      cls,
      path,
      fnames,
      label_func,
      valid_pct=0.2,
      seed=None,
      item_tfms=None,
      batch_tfms=None,
      img_cls=PILImage,
      **kwargs,
  ):
      "Create from list of `fnames` in `path`s with `label_func`"
      dblock = DataBlock(
          blocks=(ImageBlock(img_cls), CategoryBlock),
          splitter=RandomSplitter(valid_pct, seed=seed),
          get_y=label_func,
          item_tfms=item_tfms,
          batch_tfms=batch_tfms,
      )
      return cls.from_dblock(dblock, fnames, path=path, **kwargs)
  
imports: from __future__ import annotations
  from ..torch_basics import *
  from ..data.all import *
  from .core import *
  import types
label_desc: Use image node_id 2 for calling the ImageDataLoaders obj's underlying member method code with example usage: obj.from_path_func(cls, path, fnames, label_func, valid_pct, seed, item_tfms, batch_tfms, img_cls) and returns: cls