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 forward(self, x):
      return torch.relu(self.conv(x))
  
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 2 for calling the TemporalConvLayer obj's underlying member method code with example usage: obj.forward(x) and returns: torch