code: def reset_parameters(self):
      if self.in_channels > 0:
          if self.weight_initializer == "glorot":
              inits.glorot(self.weight)
          elif self.weight_initializer == "glorot_orthogonal":
              inits.glorot_orthogonal(self.weight, scale=2.0)
          elif self.weight_initializer == "uniform":
              bound = 1.0 / math.sqrt(self.weight.size(-1))
              torch.nn.init.uniform_(self.weight.data, -bound, bound)
          elif self.weight_initializer == "kaiming_uniform":
              inits.kaiming_uniform(
                  self.weight, fan=self.in_channels, a=math.sqrt(5)
              )
          elif self.weight_initializer == "zeros":
              inits.zeros(self.weight)
          elif self.weight_initializer is None:
              inits.kaiming_uniform(
                  self.weight, fan=self.in_channels, a=math.sqrt(5)
              )
          else:
              raise RuntimeError(
                  f"Linear layer weight initializer "
                  f"'{self.weight_initializer}' is not supported"
              )
  
      if self.in_channels > 0 and self.bias is not None:
          if self.bias_initializer == "zeros":
              inits.zeros(self.bias)
          elif self.bias_initializer is None:
              inits.uniform(self.in_channels, self.bias)
          else:
              raise RuntimeError(
                  f"Linear layer bias initializer "
                  f"'{self.bias_initializer}' is not supported"
              )
  
imports: from torch_cluster import radius_graph
  from torch_geometric.nn import GraphConv, GraphNorm
  from torch_geometric.nn import inits
  from torch_scatter import scatter, scatter_min
  from torch.nn import Embedding
  import torch
  from torch import nn
  from torch import Tensor
  import torch.nn.functional
  import math
  from math import sqrt
label_desc: Use image node_id 2 for calling the Linear obj's underlying member method code with example usage: obj.reset_parameters() without return types