class_docstr: An implementation of the Evolving Graph Convolutional Hidden Layer.
  For details see this paper: `"EvolveGCN: Evolving Graph Convolutional
  Networks for Dynamic Graph." <https://arxiv.org/abs/1902.10191>`_
  
  Args:
      num_of_nodes (int): Number of vertices.
      in_channels (int): Number of filters.
      improved (bool, optional): If set to :obj:`True`, the layer computes
          :math:`\mathbf{\hat{A}}` as :math:`\mathbf{A} + 2\mathbf{I}`.
          (default: :obj:`False`)
      cached (bool, optional): If set to :obj:`True`, the layer will cache
          the computation of :math:`\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}}
          \mathbf{\hat{D}}^{-1/2}` on first execution, and will use the
          cached version for further executions.
          This parameter should only be set to :obj:`True` in transductive
          learning scenarios. (default: :obj:`False`)
      normalize (bool, optional): Whether to add self-loops and apply
          symmetric normalization. (default: :obj:`True`)
      add_self_loops (bool, optional): If set to :obj:`False`, will not add
          self-loops to the input graph. (default: :obj:`True`)
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code: def reset_parameters(self):
      glorot(self.initial_weight)
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/nn/recurrent/evolvegcnh.py
imports: import torch
  from torch.nn import GRU
  from torch_geometric.nn import TopKPooling
  from .evolvegcno import glorot, GCNConv_Fixed_W
label_desc: Use image node_id 2 for calling the EvolveGCNH obj's underlying member method code with example usage: obj.reset_parameters() without return types