class_docstr: Upsampling layer with deconvolution.
  
  Args:
      upsampling_factor (int): Upsampling factor.
class_docstr_tok: ["Upsampling","layer","with","deconvolution",".","Args",":","upsampling_factor","(","int",")",":","Upsampling","factor","."]
docstr: Calculate forward propagation.
  
  Args:
      x (Tensor): Input tensor with the shape  (B, C, T)
  
  Returns:
      Tensor: Tensor with the shape (B, C, T') where T' = T * upsampling_factor.
docstr_tok: ["Calculate","forward","propagation",".","Args",":","x","(","Tensor",")",":","Input","tensor","with","the","shape","(","B",",","C",",","T",")","Returns",":","Tensor",":","Tensor","with","the","shape","(","B",",","C",",","T","'",")","where","T","'","=","T","*","upsampling_factor","."]
code: def forward(self, x):
      """Calculate forward propagation.
  
      Args:
          x (Tensor): Input tensor with the shape  (B, C, T)
  
      Returns:
          Tensor: Tensor with the shape (B, C, T') where T' = T * upsampling_factor.
  
      """
      x = x.unsqueeze(1)  # B x 1 x C x T
      x = self.conv(x)  # B x 1 x C x T'
      return x.squeeze(1)
  
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 UpSampling obj's underlying member method code with example usage: obj.forward(x) and returns: x