class_docstr: The RECT model, *i.e.* its supervised RECT-L part, from the
  `"Network Embedding with Completely-imbalanced Labels"
  <https://arxiv.org/abs/2007.03545>`_ paper.
  In particular, a GCN model is trained that reconstructs semantic class
  knowledge.
  
  .. note::
  
      For an example of using RECT, see `examples/rect.py
      <https://github.com/pyg-team/pytorch_geometric/blob/master/examples/
      rect.py>`_.
  
  Args:
      in_channels (int): Size of each input sample.
      hidden_channels (int): Intermediate size of each sample.
      normalize (bool, optional): Whether to add self-loops and compute
          symmetric normalization coefficients on-the-fly.
          (default: :obj:`True`)
      dropout (float, optional): The dropout probability.
          (default: :obj:`0.0`)
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code: def forward(
      self,
      x: Tensor,
      edge_index: Adj,
      edge_weight: OptTensor = None,
  ) -> Tensor:
      """"""  # noqa: D419
      x = self.conv(x, edge_index, edge_weight)
      x = F.dropout(x, p=self.dropout, training=self.training)
      return self.lin(x)
  
file_path: pytorch_geometric/torch_geometric/nn/models/rect.py
imports: import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import Linear
  from torch_geometric.nn import GCNConv
  from torch_geometric.typing import Adj, OptTensor
  from torch_geometric.utils import scatter
label_desc: Use image node_id 3 for calling the RECT_L obj's underlying member method code with example usage: obj.forward(x, edge_index, edge_weight) and returns: self