class_docstr: Temporal convolution layer.
  
  arguments
  ---------
  c_in : int
      The number of input channels (features)
  c_out : int
      The number of output channels (features)
  dia : int
      The dilation size
class_docstr_tok: ["Temporal","convolution","layer",".","arguments","--","--","--","--","-","c_in",":","int","The","number","of","input","channels","(","features",")","c_out",":","int","The","number","of","output","channels","(","features",")","dia",":","int","The","dilation","size"]
code: def __init__(self, c_in, c_out, dia=1):
      super(TemporalConvLayer, self).__init__()
      self.c_out = c_out
      self.c_in = c_in
      self.conv = nn.Conv2d(
          c_in, c_out, (2, 1), 1, dilation=dia, padding=(0, 0)
      )
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.nn.init
  from dgl.nn.pytorch import GraphConv
  from dgl.nn.pytorch.conv import ChebConv
label_desc: Use image node_id 1 to create a new TemporalConvLayer object from inherited base classes: nn with example: obj = TemporalConvLayer(c_in, c_out, dia)