class_docstr: An implementation of the dilated inception 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.
      kernel_set (list of int): List of kernel sizes.
      dilated_factor (int, optional): Dilation factor.
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code: def __init__(
      self,
      c_in: int,
      c_out: int,
      kernel_set: list,
      dilation_factor: int,
  ):
      super(DilatedInception, self).__init__()
      self._time_conv = nn.ModuleList()
      self._kernel_set = kernel_set
      c_out = int(c_out / len(self._kernel_set))
      for kern in self._kernel_set:
          self._time_conv.append(
              nn.Conv2d(
                  c_in, c_out, (1, kern), dilation=(1, dilation_factor)
              )
          )
      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 DilatedInception object from inherited base classes: nn with example: obj = DilatedInception(c_in, c_out, kernel_set, dilation_factor)