class_docstr: An implementation of the the Integrated Graph Convolutional Long Short Term
  Memory Cell. For details see this paper: `"GC-LSTM: Graph Convolution Embedded LSTM
  for Dynamic Link Prediction." <https://arxiv.org/abs/1812.04206>`_
  
  Args:
      in_channels (int): Number of input features.
      out_channels (int): Number of output features.
      K (int): Chebyshev filter size :math:`K`.
      normalization (str, optional): The normalization scheme for the graph
          Laplacian (default: :obj:`"sym"`):
  
          1. :obj:`None`: No normalization
          :math:`\mathbf{L} = \mathbf{D} - \mathbf{A}`
  
          2. :obj:`"sym"`: Symmetric normalization
          :math:`\mathbf{L} = \mathbf{I} - \mathbf{D}^{-1/2} \mathbf{A}
          \mathbf{D}^{-1/2}`
  
          3. :obj:`"rw"`: Random-walk normalization
          :math:`\mathbf{L} = \mathbf{I} - \mathbf{D}^{-1} \mathbf{A}`
  
          You need to pass :obj:`lambda_max` to the :meth:`forward` method of
          this operator in case the normalization is non-symmetric.
          :obj:`\lambda_max` should be a :class:`torch.Tensor` of size
          :obj:`[num_graphs]` in a mini-batch scenario and a
          scalar/zero-dimensional tensor when operating on single graphs.
          You can pre-compute :obj:`lambda_max` via the
          :class:`torch_geometric.transforms.LaplacianLambdaMax` transform.
      bias (bool, optional): If set to :obj:`False`, the layer will not learn
          an additive bias. (default: :obj:`True`)
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code: def _create_forget_gate_parameters_and_layers(self):
      self.conv_f = ChebConv(
          in_channels=self.out_channels,
          out_channels=self.out_channels,
          K=self.K,
          normalization=self.normalization,
          bias=self.bias,
      )
  
      self.W_f = Parameter(
          torch.Tensor(self.in_channels, self.out_channels)
      )
      self.b_f = Parameter(torch.Tensor(1, self.out_channels))
  
imports: import torch
  from torch.nn import Parameter
  from torch_geometric.nn import ChebConv
  from torch_geometric.nn.inits import glorot, zeros
label_desc: Use image node_id 3 for calling the GCLSTM obj's underlying member method code with example usage: obj._create_forget_gate_parameters_and_layers() without return types