class_docstr: An implementation of the graph learning layer to construct an adjacency matrix.
  For details see this paper: `"Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks."
  <https://arxiv.org/pdf/2005.11650.pdf>`_
  
  Args:
      nnodes (int): Number of nodes in the graph.
      k (int): Number of largest values to consider in constructing the neighbourhood of a node (pick the "nearest" k nodes).
      dim (int): Dimension of the node embedding.
      alpha (float, optional): Tanh alpha for generating adjacency matrix, alpha controls the saturation rate
      xd (int, optional): Static feature dimension, default None.
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code: def _reset_parameters(self):
      for p in self.parameters():
          if p.dim() > 1:
              nn.init.xavier_uniform_(p)
          else:
              nn.init.uniform_(p)
  
imports: from __future__ import division
  import numbers
  from typing import Optional
  import torch
  import torch.nn
  from torch.nn import init
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the GraphConstructor obj's underlying member method code with example usage: obj._reset_parameters() without return types