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","."]
code: def __init__(self, upsampling_factor, bias=True):
      super(UpSampling, self).__init__()
      self.upsampling_factor = upsampling_factor
      self.bias = bias
      self.conv = nn.ConvTranspose2d(
          1,
          1,
          kernel_size=(1, self.upsampling_factor),
          stride=(1, self.upsampling_factor),
          bias=self.bias,
      )
  
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 1 to create a new UpSampling object from inherited base classes: nn with example: obj = UpSampling(upsampling_factor, bias)