class_docstr: See :class:`torch_geometric.nn.conv.GCNConv`.
class_docstr_tok: ["See",":","class",":","`","torch_geometric.nn.conv.GCNConv","`","."]
code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      improved: bool = False,
      bias: bool = True,
  ):
      super().__init__()
  
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.improved = improved
  
      self.lin = Linear(
          in_channels,
          out_channels,
          bias=False,
          weight_initializer="glorot",
      )
  
      if bias:
          self.bias = Parameter(torch.empty(out_channels))
      else:
          self.register_parameter("bias", None)
  
      self.reset_parameters()
  
file_path: pytorch_geometric/torch_geometric/nn/dense/dense_gcn_conv.py
imports: import torch
  from torch import Tensor
  from torch.nn import Parameter
  from torch_geometric.nn.dense.linear import Linear
  from torch_geometric.nn.inits import zeros
  from torch_geometric.typing import OptTensor
label_desc: Use image node_id 1 to create a new DenseGCNConv object from inherited base classes: torch.nn with example: obj = DenseGCNConv(in_channels, out_channels, improved, bias)