code: def render_vis(
      model,
      objective_f,
      param_f=None,
      optimizer=None,
      transforms=None,
      thresholds=(512,),
      print_objectives=None,
      verbose=True,
      relu_gradient_override=True,
      use_fixed_seed=False,
  ):
      """Flexible optimization-based feature vis.
  
      There's a lot of ways one might wish to customize optimization-based
      feature visualization. It's hard to create an abstraction that stands up
      to all the things one might wish to try.
  
      This function probably can't do *everything* you want, but it's much more
      flexible than a naive attempt. The basic abstraction is to split the problem
      into several parts. Consider the arguments:
  
      Args:
        model: The model to be visualized, from Alex' modelzoo.
        objective_f: The objective our visualization maximizes.
          See the objectives module for more details.
        param_f: Paramaterization of the image we're optimizing.
          See the paramaterization module for more details.
          Defaults to a naively paramaterized [1, 128, 128, 3] image.
        optimizer: Optimizer to optimize with. Either tf.train.Optimizer instance,
          or a function from (graph, sess) to such an instance.
          Defaults to Adam with lr .05.
        transforms: A list of stochastic transformations that get composed,
          which our visualization should robustly activate the network against.
          See the transform module for more details.
          Defaults to [transform.jitter(8)].
        thresholds: A list of numbers of optimization steps, at which we should
          save (and display if verbose=True) the visualization.
        print_objectives: A list of objectives separate from those being optimized,
          whose values get logged during the optimization.
        verbose: Should we display the visualization when we hit a threshold?
          This should only be used in IPython.
        relu_gradient_override: Whether to use the gradient override scheme
          described in lucid/misc/redirected_relu_grad.py. On by default!
        use_fixed_seed: Seed the RNG with a fixed value so results are reproducible.
          Off by default. As of tf 1.8 this does not work as intended, see:
          https://github.com/tensorflow/tensorflow/issues/9171
  
      Returns:
        2D array of optimization results containing of evaluations of supplied
        param_f snapshotted at specified thresholds. Usually that will mean one or
        multiple channel visualizations stacked on top of each other.
      """
  
      with tf.Graph().as_default() as graph, tf.Session() as sess:
          if (
              use_fixed_seed
          ):  # does not mean results are reproducible, see Args doc
              tf.set_random_seed(0)
  
          T = make_vis_T(
              model,
              objective_f,
              param_f,
              optimizer,
              transforms,
              relu_gradient_override,
          )
          print_objective_func = make_print_objective_func(
              print_objectives, T
          )
          loss, vis_op, t_image = T("loss"), T("vis_op"), T("input")
          tf.global_variables_initializer().run()
  
          images = []
          try:
              for i in range(max(thresholds) + 1):
                  loss_, _ = sess.run([loss, vis_op])
                  if i in thresholds:
                      vis = t_image.eval()
                      images.append(vis)
                      if verbose:
                          print(i, loss_)
                          print_objective_func(sess)
                          show(np.hstack(vis))
          except KeyboardInterrupt:
              log.warning(
                  "Interrupted optimization at step {:d}.".format(i + 1)
              )
              vis = t_image.eval()
              show(np.hstack(vis))
  
          return images
  
file_path: lucid/lucid/optvis/render.py
imports: from __future__ import absolute_import, division, print_function
  from future.standard_library import install_aliases
  from builtins import range
  import numpy
  import tensorflow
  import logging
  from lucid.optvis import objectives, param, transform
  from lucid.misc.io import show
  from lucid.misc.redirected_relu_grad import redirected_relu_grad, redirected_relu6_grad
  from lucid.misc.gradient_override import gradient_override_map
label_desc: Use image node_id 1 for calling a global function with example usage: render_vis(model, objective_f, param_f, optimizer, transforms, thresholds, print_objectives, verbose, relu_gradient_override, use_fixed_seed) and returns: images