code: def __init__(
      self,
      target,
  ):
      super(ContentLoss, self).__init__()
      # we 'detach' the target content from the tree used
      # to dynamically compute the gradient: this is a stated value,
      # not a variable. Otherwise the forward method of the criterion
      # will throw an error.
      self.target = target.detach()
  
imports: from __future__ import print_function
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from PIL import Image
  import matplotlib.pyplot
  import torchvision.transforms
  import torchvision.models
  import copy
  from clearml import Task
label_desc: Use image node_id 1 to create a new ContentLoss object from inherited base classes: nn with example: obj = ContentLoss(target)