code: def get_graph_drop_transform(drop_edge_p, feat_mask_p):
      transforms = list()
  
      # make copy of graph
      transforms.append(copy.deepcopy)
  
      # drop edges
      if drop_edge_p > 0.0:
          transforms.append(DropEdge(drop_edge_p))
  
      # drop features
      if feat_mask_p > 0.0:
          transforms.append(
              FeatMask(feat_mask_p, node_feat_names=["feat"])
          )
  
      return Compose(transforms)
  
file_path: dgl/examples/pytorch/bgrl/utils.py
imports: import copy
  import numpy
  import torch
  from dgl.data import AmazonCoBuyComputerDataset, AmazonCoBuyPhotoDataset, CoauthorCSDataset, CoauthorPhysicsDataset, PPIDataset, WikiCSDataset
  from dgl.dataloading import GraphDataLoader
  from dgl.transforms import Compose, DropEdge, FeatMask, RowFeatNormalizer
label_desc: Use image node_id 1 for calling a global function with example usage: get_graph_drop_transform(drop_edge_p, feat_mask_p) and returns: Compose