class_docstr: Definition of the DXtrem network from :cite:`xsoria2020dexined`.
  
  Return:
      A list of tensor with the intermediate features which the last element
      is the edges map with shape :math:`(B,1,H,W)`.
  
  Example:
      >>> img = torch.rand(1, 3, 320, 320)
      >>> net = DexiNed(pretrained=False)
      >>> out = net(img)
      >>> out[-1].shape
      torch.Size([1, 1, 320, 320])
class_docstr_tok: ["Definition","of","the","DXtrem","network","from",":","cite",":","`","xsoria2020dexined","`",".","Return",":","A","list","of","tensor","with","the","intermediate","features","which","the","last","element","is","the","edges","map","with","shape",":","math",":","`","(","B,1",",","H",",","W",")","`",".","Example",":",">",">",">","img","=","torch.rand","(","1",",","3",",","320",",","320",")",">",">",">","net","=","DexiNed","(","pretrained=False",")",">",">",">","out","=","net","(","img",")",">",">",">","out","[","-1","]",".shape","torch.Size","(","[","1",",","1",",","320",",","320","]",")"]
code: def load_from_file(self, path_file: str) -> None:
      # use torch.hub to load pretrained model
      pretrained_dict = torch.hub.load_state_dict_from_url(
          path_file, map_location=map_location_to_cpu
      )
      self.load_state_dict(pretrained_dict, strict=True)
      self.eval()
  
imports: from __future__ import annotations
  from collections import OrderedDict
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import nn
  from kornia.core import Module, Tensor, concatenate
  from kornia.core.check import KORNIA_CHECK
  from kornia.utils import map_location_to_cpu
label_desc: Use image node_id 2 for calling the DexiNed obj's underlying member method code with example usage: obj.load_from_file(path_file) without return types