class_docstr: An implementation of the linear layer, conducting 2D convolution.
  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.
      bias (bool, optional): Whether to have bias. Default: True.
class_docstr_tok: ["An","implementation","of","the","linear","layer",",","conducting","2D","convolution",".","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",".","bias","(","bool",",","optional",")",":","Whether","to","have","bias",".","Default",":","True","."]
code: def __init__(self, c_in: int, c_out: int, bias: bool = True):
      super(Linear, self).__init__()
      self._mlp = torch.nn.Conv2d(
          c_in,
          c_out,
          kernel_size=(1, 1),
          padding=(0, 0),
          stride=(1, 1),
          bias=bias,
      )
  
      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 Linear object from inherited base classes: nn with example: obj = Linear(c_in, c_out, bias)