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.
class_docstr_tok: ["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","."]
docstr: Making a forward pass of dilated inception.
  
  Arg types:
      * **X_in** (Pytorch Float Tensor) - Input feature Tensor, with shape (batch_size, c_in, num_nodes, seq_len).
  
  Return types:
      * **X** (PyTorch Float Tensor) - Hidden representation for all nodes,
      with shape (batch_size, c_out, num_nodes, seq_len-6).
code: def forward(self, X_in: torch.FloatTensor) -> torch.FloatTensor:
      """
      Making a forward pass of dilated inception.
  
      Arg types:
          * **X_in** (Pytorch Float Tensor) - Input feature Tensor, with shape (batch_size, c_in, num_nodes, seq_len).
  
      Return types:
          * **X** (PyTorch Float Tensor) - Hidden representation for all nodes,
          with shape (batch_size, c_out, num_nodes, seq_len-6).
      """
      X = []
      for i in range(len(self._kernel_set)):
          X.append(self._time_conv[i](X_in))
      for i in range(len(self._kernel_set)):
          X[i] = X[i][..., -X[-1].size(3) :]
      X = torch.cat(X, dim=1)
      return X
  
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 3 for calling the DilatedInception obj's underlying member method code with example usage: obj.forward(X_in) and returns: X