class_docstr: Temporal convolution block applied to nodes in the STGCN Layer
  For details see: `"Spatio-Temporal Graph Convolutional Networks:
  A Deep Learning Framework for Traffic Forecasting."
  <https://arxiv.org/abs/1709.04875>`_ Based off the temporal convolution
   introduced in "Convolutional Sequence to Sequence Learning"  <https://arxiv.org/abs/1709.04875>`_
  
  Args:
      in_channels (int): Number of input features.
      out_channels (int): Number of output features.
      kernel_size (int): Convolutional kernel size.
class_docstr_tok: ["Temporal","convolution","block","applied","to","nodes","in","the","STGCN","Layer","For","details","see",":","`","``","Spatio-Temporal","Graph","Convolutional","Networks",":","A","Deep","Learning","Framework","for","Traffic","Forecasting",".","''","<","https",":","\/\/arxiv.org\/abs\/1709.04875",">","`","_","Based","off","the","temporal","convolution","introduced","in","``","Convolutional","Sequence","to","Sequence","Learning","''","<","https",":","\/\/arxiv.org\/abs\/1709.04875",">","`","_","Args",":","in_channels","(","int",")",":","Number","of","input","features",".","out_channels","(","int",")",":","Number","of","output","features",".","kernel_size","(","int",")",":","Convolutional","kernel","size","."]
code: def __init__(
      self, in_channels: int, out_channels: int, kernel_size: int = 3
  ):
      super(TemporalConv, self).__init__()
      self.conv_1 = nn.Conv2d(
          in_channels, out_channels, (1, kernel_size)
      )
      self.conv_2 = nn.Conv2d(
          in_channels, out_channels, (1, kernel_size)
      )
      self.conv_3 = nn.Conv2d(
          in_channels, out_channels, (1, kernel_size)
      )
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/nn/attention/stgcn.py
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from torch_geometric.nn import ChebConv
label_desc: Use image node_id 1 to create a new TemporalConv object from inherited base classes: nn with example: obj = TemporalConv(in_channels, out_channels, kernel_size)