class_docstr: An implementation of the dynatic mix-hop propagation 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:
      c_in (int): Number of input channels.
      c_out (int): Number of output channels.
      gdep (int): Depth of graph convolution.
      dropout (float): Dropout rate.
      alpha (float): Ratio of retaining the root nodes's original states, 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 MixProp obj's underlying member method code with example usage: obj._reset_parameters() without return types