class_docstr: Graph convolution module used in the GCMC model.
  
  Parameters
  ----------
  in_feats : int
      Input feature size.
  out_feats : int
      Output feature size.
  weight : bool, optional
      If True, apply a linear layer. Otherwise, aggregating the messages
      without a weight matrix or with an shared weight provided by caller.
  device: str, optional
      Which device to put data in. Useful in mix_cpu_gpu training and
      multi-gpu training
class_docstr_tok: ["Graph","convolution","module","used","in","the","GCMC","model",".","Parameters","--","--","--","--","--","in_feats",":","int","Input","feature","size",".","out_feats",":","int","Output","feature","size",".","weight",":","bool",",","optional","If","True",",","apply","a","linear","layer",".","Otherwise",",","aggregating","the","messages","without","a","weight","matrix","or","with","an","shared","weight","provided","by","caller",".","device",":","str",",","optional","Which","device","to","put","data","in",".","Useful","in","mix_cpu_gpu","training","and","multi-gpu","training"]
docstr: Reinitialize learnable parameters.
docstr_tok: ["Reinitialize","learnable","parameters","."]
code: def reset_parameters(self):
      """Reinitialize learnable parameters."""
      if self.weight is not None:
          init.xavier_uniform_(self.weight)
  
imports: import dgl.function
  import dgl.nn.pytorch
  from dgl import DGLError
  import torch
  import torch.nn
  from torch.nn import init
  from .utils import get_activation
label_desc: Use image node_id 2 for calling the GCMCGraphConv obj's underlying member method code with example usage: obj.reset_parameters() without return types