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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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-base feature vis. There's a lot of ways one might wish to customize otpimization-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 rguments: 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