class_docstr: Transform function container.
  
  lambda can't work well when using DDP because
  lambda is not pickable in the case of multi process.
  This class is required for DDP use case.
  
  Args:
      converter: batch converter
      load: function object to load data and create minibatch
class_docstr_tok: ["Transform","function","container",".","lambda","ca","n't","work","well","when","using","DDP","because","lambda","is","not","pickable","in","the","case","of","multi","process",".","This","class","is","required","for","DDP","use","case",".","Args",":","converter",":","batch","converter","load",":","function","object","to","load","data","and","create","minibatch"]
docstr: Apply a given converter and a given loader.
docstr_tok: ["Apply","a","given","converter","and","a","given","loader","."]
code: def __call__(self, data):
      """Apply a given converter and a given loader."""
      return self._converter([self._load(data)])
  
file_path: espnet/espnet/utils/dataset.py
imports: import torch
  import torch.utils.data
label_desc: Use image node_id 2 for calling the Transform obj's underlying member method code with example usage: obj.__call__(data) and returns: self