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.
code: def __init__(
      self, augmenter, device, pre_trans=None, post_trans=None
  ):
      self.augmenter = augmenter
      self.pre_trans = pre_trans
      self.post_trans = post_trans
      self.device = device
  
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 1 to create a new AUG_trans object from inherited base classes: object with example: obj = AUG_trans(augmenter, device, pre_trans, post_trans)