class_docstr: This class generates an augmentation from a given sample.
  
  Args:
      augmenter (function): This method generates an augmentation from the
          given sample.
      device (str): The device on which the data will be processed.
      pre_trans (function, optional): This transformation is applied on the
          original sample before an augmentation is generated. Default is
          None.
      post_trans (function, optional): This transformation is applied on the
          generated augmented sample. Default is None.
docstr: This is the main function that generates an augmentation from a given
  sample.
  
  Args:
      data: The given data sample.
  Returns:
      A transformed graph.
docstr_tok: ["This","is","the","main","function","that","generates","an","augmentation","from","a","given","sample",".","Args",":","data",":","The","given","data","sample",".","Returns",":","A","transformed","graph","."]
code: def __call__(self, data):
      r"""
      This is the main function that generates an augmentation from a given
      sample.
  
      Args:
          data: The given data sample.
      Returns:
          A transformed graph.
      """
      if self.pre_trans:
          data = self.pre_trans(data)
      new_data = self.augmenter(data)[0]
      if self.post_trans:
          new_data = self.post_trans(new_data)
      return new_data
  
imports: import random
  import torch
  import torch.nn.functional
  from torch.utils.data import Dataset
  from torch_geometric.utils import degree
label_desc: Use image node_id 2 for calling the AUG_trans obj's underlying member method code with example usage: obj.__call__(data) and returns: new_data