class_docstr: 1D dilated causal convolution.
class_docstr_tok: ["1D","dilated","causal","convolution","."]
docstr: Calculate forward propagation.
  
  Args:
      x (Tensor): Input tensor with the shape (B, in_channels, T).
  
  Returns:
      Tensor: Tensor with the shape (B, out_channels, T)
docstr_tok: ["Calculate","forward","propagation",".","Args",":","x","(","Tensor",")",":","Input","tensor","with","the","shape","(","B",",","in_channels",",","T",")",".","Returns",":","Tensor",":","Tensor","with","the","shape","(","B",",","out_channels",",","T",")"]
code: def forward(self, x):
      """Calculate forward propagation.
  
      Args:
          x (Tensor): Input tensor with the shape (B, in_channels, T).
  
      Returns:
          Tensor: Tensor with the shape (B, out_channels, T)
  
      """
      x = self.conv(x)
      if self.padding != 0:
          x = x[:, :, : -self.padding]
      return x
  
imports: import logging
  import sys
  import time
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the CausalConv1d obj's underlying member method code with example usage: obj.forward(x) and returns: x