class_docstr: Model allows using pre-trained models.
class_docstr_tok: ["Model","allows","using","pre-trained","models","."]
code: def load_from_graphdef(graphdef_url):
      graph_def = load(graphdef_url)
      metadata = model_util.extract_metadata(graph_def)
      if metadata:
          return Model.load_from_metadata(graphdef_url, metadata)
      else:
          raise ValueError(
              "Model.load_from_graphdef was called on a GraphDef ({}) that does not contain Lucid's metadata node. Model.load only works for models saved via Model.save. For the graphdef you're trying to load, you will need to provide custom metadata; see Model.load_from_metadata()".format(
                  graphdef_url
              )
          )
  
imports: from __future__ import absolute_import, division, print_function
  from os import path
  import warnings
  import logging
  from itertools import chain
  import tensorflow
  import numpy
  from lucid.modelzoo import util
  from lucid.modelzoo.aligned_activations import get_aligned_activations
  from lucid.modelzoo.get_activations import get_activations
  from lucid.misc.io import load, save
  import lucid.misc.io.showing
label_desc: Use image node_id 22 for calling the Model obj's underlying member method code with example usage: obj.load_from_graphdef(graphdef_url) and returns: Model