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 `df` using `fn_col` and `label_col`
docstr_tok: ["Create","from","`","df","`","using","`","fn_col","`","and","`","label_col","`"]
code: def from_df(
      cls,
      df,
      path=".",
      valid_pct=0.2,
      seed=None,
      fn_col=0,
      folder=None,
      suff="",
      label_col=1,
      label_delim=None,
      y_block=None,
      valid_col=None,
      item_tfms=None,
      batch_tfms=None,
      img_cls=PILImage,
      **kwargs,
  ):
      "Create from `df` using `fn_col` and `label_col`"
      pref = f"{Path(path) if folder is None else Path(path)/folder}{os.path.sep}"
      if y_block is None:
          is_multi = (
              is_listy(label_col) and len(label_col) > 1
          ) or label_delim is not None
          y_block = MultiCategoryBlock if is_multi else CategoryBlock
      splitter = (
          RandomSplitter(valid_pct, seed=seed)
          if valid_col is None
          else ColSplitter(valid_col)
      )
      dblock = DataBlock(
          blocks=(ImageBlock(img_cls), y_block),
          get_x=ColReader(fn_col, pref=pref, suff=suff),
          get_y=ColReader(label_col, label_delim=label_delim),
          splitter=splitter,
          item_tfms=item_tfms,
          batch_tfms=batch_tfms,
      )
      return cls.from_dblock(dblock, df, 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 6 for calling the ImageDataLoaders obj's underlying member method code with example usage: obj.from_df(cls, df, path, valid_pct, seed, fn_col, folder, suff, label_col, label_delim, y_block, valid_col, item_tfms, batch_tfms, img_cls) and returns: cls