class_docstr: Wrapper class for data config YAML
class_docstr_tok: ["Wrapper","class","for","data","config","YAML"]
docstr: Split-specific feature transforms. Allowing train set
  wildcard `_train`, evaluation set wildcard `_eval` and general
  wildcard `*` for matching.
docstr_tok: ["Split-specific","feature","transforms",".","Allowing","train","set","wildcard","`","_train","`",",","evaluation","set","wildcard","`","_eval","`","and","general","wildcard","`","*","`","for","matching","."]
code: def get_transforms(self, transform_type, split, is_train):
      """Split-specific feature transforms. Allowing train set
      wildcard `_train`, evaluation set wildcard `_eval` and general
      wildcard `*` for matching."""
      from copy import deepcopy
  
      cfg = deepcopy(self.config)
      _cur = cfg.get(f"{transform_type}transforms", {})
      cur = _cur.get(split)
      cur = _cur.get("_train") if cur is None and is_train else cur
      cur = _cur.get("_eval") if cur is None and not is_train else cur
      cur = _cur.get("*") if cur is None else cur
      return cur
  
imports: import logging
  from argparse import Namespace
  from copy import deepcopy
  from pathlib import Path
  from typing import Dict, Optional
  from fairseq.data import Dictionary
label_desc: Use image node_id 18 for calling the S2TDataConfig obj's underlying member method code with example usage: obj.get_transforms(transform_type, split, is_train) and returns: cur