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` and `labels` in `path`
docstr_tok: ["Create","from","list","of","`","fnames","`","and","`","labels","`","in","`","path","`"]
code: def from_lists(
      cls,
      path,
      fnames,
      labels,
      valid_pct=0.2,
      seed: int = None,
      y_block=None,
      item_tfms=None,
      batch_tfms=None,
      img_cls=PILImage,
      **kwargs,
  ):
      "Create from list of `fnames` and `labels` in `path`"
      if y_block is None:
          y_block = (
              MultiCategoryBlock
              if is_listy(labels[0]) and len(labels[0]) > 1
              else (
                  RegressionBlock
                  if isinstance(labels[0], float)
                  else CategoryBlock
              )
          )
      dblock = DataBlock.from_columns(
          blocks=(ImageBlock(img_cls), y_block),
          splitter=RandomSplitter(valid_pct, seed=seed),
          item_tfms=item_tfms,
          batch_tfms=batch_tfms,
      )
      return cls.from_dblock(
          dblock, (fnames, labels), 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 8 for calling the ImageDataLoaders obj's underlying member method code with example usage: obj.from_lists(cls, path, fnames, labels, valid_pct, seed, y_block, item_tfms, batch_tfms, img_cls) and returns: cls