class_docstr: One layer of CARE-GNN.
class_docstr_tok: ["One","layer","of","CARE-GNN","."]
code: def __init__(
      self,
      in_dim,
      out_dim,
      num_classes,
      edges,
      activation=None,
      step_size=0.02,
  ):
      super(CAREConv, self).__init__()
  
      self.activation = activation
      self.step_size = step_size
      self.in_dim = in_dim
      self.out_dim = out_dim
      self.num_classes = num_classes
      self.edges = edges
  
      self.linear = nn.Linear(self.in_dim, self.out_dim)
      self.MLP = nn.Linear(self.in_dim, self.num_classes)
  
      self.p = {}
      self.last_avg_dist = {}
      self.f = {}
      # indicate whether the RL converges
      self.cvg = {}
      for etype in edges:
          self.p[etype] = 0.5
          self.last_avg_dist[etype] = 0
          self.f[etype] = []
          self.cvg[etype] = False
  
imports: import dgl
  import dgl.function
  import numpy
  import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new CAREConv object from inherited base classes: nn with example: obj = CAREConv(in_dim, out_dim, num_classes, edges, activation, step_size)