class_docstr: An implementation of the Node Adaptive Graph Convolution Layer.
  For details see: `"Adaptive Graph Convolutional Recurrent Network
  for Traffic Forecasting" <https://arxiv.org/abs/2007.02842>`_
  Args:
      in_channels (int): Number of input features.
      out_channels (int): Number of output features.
      K (int): Filter size :math:`K`.
      embedding_dimensions (int): Number of node embedding dimensions.
code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      K: int,
      embedding_dimensions: int,
  ):
      super(AVWGCN, self).__init__()
      self.K = K
      self.weights_pool = torch.nn.Parameter(
          torch.Tensor(
              embedding_dimensions, K, in_channels, out_channels
          )
      )
      self.bias_pool = torch.nn.Parameter(
          torch.Tensor(embedding_dimensions, out_channels)
      )
      glorot(self.weights_pool)
      zeros(self.bias_pool)
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/nn/recurrent/agcrn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch_geometric.nn.inits import glorot, zeros
label_desc: Use image node_id 1 to create a new AVWGCN object from inherited base classes: nn with example: obj = AVWGCN(in_channels, out_channels, K, embedding_dimensions)