class_docstr: Adaptive augmentation methods proposed in the paper `Graph Contrastive Learning
  with Adaptive Augmentation <https://arxiv.org/abs/2010.14945>`_. You can refer to `the original implementation 
  <https://github.com/CRIPAC-DIG/GCA>` or `the benchmark code 
  <https://github.com/divelab/DIG/blob/dig/benchmarks/sslgraph/example_grace.ipynb>`_ for
  an example of usage.
  
  *Alias*: :obj:`dig.sslgraph.method.contrastive.model.`:obj:`pGRACE`.
      
  Args:
      dim (int): The embedding dimension.
      proj_n_dim (int): The projection head dimension to use for computing loss
      centrality_measure (str): The metric to use for computing edge or node centrality. Supported values are `degree`, `evc` (Eigen-Vector centrality) and `pr` (PageRank centrality).
      prob_edge_1, prob_edge_2 (float): The probability factor for calculating edge-drop probability
      prob_feature_1, prob_feature_2 (float): The probability factor for calculating feature-masking probability
      tau (float, optional): The temperature parameter used for contrastive objective.
      dense (bool, optional): Whether the node features are dense continuous features. Defaults to `false`.
      p_tau (float, optional): The upper-bound probability for dropping edges or removing nodes.
      **kwargs (optional): Additional arguments of :class:`dig.sslgraph.method.Contrastive`.
code: def train(
      self,
      encoders,
      data_loader,
      optimizer,
      epochs,
      per_epoch_out=False,
  ):
      # GRACE removes projection heads after pre-training
      for enc, proj in super().train(
          encoders, data_loader, optimizer, epochs, per_epoch_out
      ):
          yield enc
  
imports: from .contrastive import Contrastive
  from dig.sslgraph.method.contrastive.views_fn import AdaNodeAttrMask, AdaEdgePerturbation, Sequential
  import torch
label_desc: Use image node_id 3 for calling the pGRACE obj's underlying member method code with example usage: obj.train(encoders, data_loader, optimizer, epochs, per_epoch_out) without return types