class_docstr: An implementation of the Adaptive Graph Convolutional Recurrent Unit.
  For details see: `"Adaptive Graph Convolutional Recurrent Network
  for Traffic Forecasting" <https://arxiv.org/abs/2007.02842>`_
  Args:
      number_of_nodes (int): Number of vertices.
      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,
      number_of_nodes: int,
      in_channels: int,
      out_channels: int,
      K: int,
      embedding_dimensions: int,
  ):
      super(AGCRN, self).__init__()
  
      self.number_of_nodes = number_of_nodes
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.K = K
      self.embedding_dimensions = embedding_dimensions
      self._setup_layers()
  
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 AGCRN object from inherited base classes: nn with example: obj = AGCRN(number_of_nodes, in_channels, out_channels, K, embedding_dimensions)