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 __init__(
      self,
      c_in: int,
      c_out: int,
      gdep: int,
      dropout: float,
      alpha: float,
  ):
      super(MixProp, self).__init__()
      self._mlp = Linear((gdep + 1) * c_in, c_out)
      self._gdep = gdep
      self._dropout = dropout
      self._alpha = alpha
  
      self._reset_parameters()
  
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 1 to create a new MixProp object from inherited base classes: nn with example: obj = MixProp(c_in, c_out, gdep, dropout, alpha)