class_docstr: An implementation of the MTGNN layer.
  For details see this paper: `"Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks."
  <https://arxiv.org/pdf/2005.11650.pdf>`_
  
  Args:
      dilation_exponential (int): Dilation exponential.
      rf_size_i (int): Size of receptive field.
      kernel_size (int): Size of kernel for convolution, to calculate receptive field size.
      j (int): Iteration index.
      residual_channels (int): Residual channels.
      conv_channels (int): Convolution channels.
      skip_channels (int): Skip channels.
      kernel_set (list of int): List of kernel sizes.
      new_dilation (int): Dilation.
      layer_norm_affline (bool): Whether to do elementwise affine in Layer Normalization.
      gcn_true (bool): Whether to add graph convolution layer.
      seq_length (int): Length of input sequence.
      receptive_field (int): Receptive field.
      dropout (float): Droupout rate.
      gcn_depth (int): Graph convolution depth.
      num_nodes (int): Number of nodes in the graph.
      propalpha (float): Prop alpha, ratio of retaining the root nodes's original states in mix-hop propagation, a value between 0 and 1.
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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 MTGNNLayer obj's underlying member method code with example usage: obj._reset_parameters() without return types