class_docstr: Dense bi-linear decoder.
  
  Dense implementation of the bi-linear decoder used in GCMC. Suitable when
  the graph can be efficiently represented by a pair of arrays (one for
  source nodes; one for destination nodes).
  
  Parameters
  ----------
  in_units : int
      Size of input user and item features
  num_classes : int
      Number of classes.
  num_basis : int, optional
      Number of basis. (Default: 2)
  dropout_rate : float, optional
      Dropout raite (Default: 0.0)
class_docstr_tok: ["Dense","bi-linear","decoder",".","Dense","implementation","of","the","bi-linear","decoder","used","in","GCMC",".","Suitable","when","the","graph","can","be","efficiently","represented","by","a","pair","of","arrays","(","one","for","source","nodes",";","one","for","destination","nodes",")",".","Parameters","--","--","--","--","--","in_units",":","int","Size","of","input","user","and","item","features","num_classes",":","int","Number","of","classes",".","num_basis",":","int",",","optional","Number","of","basis",".","(","Default",":","2",")","dropout_rate",":","float",",","optional","Dropout","raite","(","Default",":","0.0",")"]
docstr: Reset parameters to uniform distribution
docstr_tok: ["Reset","parameters","to","uniform","distribution"]
code: def reset_parameters(self):
      """Reset parameters to uniform distribution"""
      for param in self.parameters():
          if param.dim() > 1:
              nn.init.xavier_uniform_(param)
  
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 DenseBiDecoder obj's underlying member method code with example usage: obj.reset_parameters() without return types