human
Solve the following python programming problem given the following code:
# @title Imports and utility functions

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import IPython
from IPython.display import display
import numpy as np
import PIL.Image
import pandas as pd
import six
import tensorflow as tf
import tensorflow_hub as hub

def imgrid(imarray, cols=8, pad=1):
  pad = int(pad)
  assert pad >= 0
  cols = int(cols)
  assert cols >= 1
  N, H, W, C = imarray.shape
  rows = int(np.ceil(N / float(cols)))
  batch_pad = rows * cols - N
  assert batch_pad >= 0
  post_pad = [batch_pad, pad, pad, 0]
  pad_arg = [[0, p] for p in post_pad]
  imarray = np.pad(imarray, pad_arg, 'constant')
  H += pad
  W += pad
  grid = (imarray
          .reshape(rows, cols, H, W, C)
          .transpose(0, 2, 1, 3, 4)
          .reshape(rows*H, cols*W, C))
  return grid[:-pad, :-pad]

def imshow(a, format='png', jpeg_fallback=True):
  a = np.asarray(a, dtype=np.uint8)
  if six.PY3:
    str_file = six.BytesIO()
  else:
    str_file = six.StringIO()
  PIL.Image.fromarray(a).save(str_file, format)
  png_data = str_file.getvalue()
  try:
    disp = display(IPython.display.Image(png_data))
  except IOError:
    if jpeg_fallback and format != 'jpeg':
      print ('Warning: image was too large to display in format "{}".
'
             'trying jpeg instead.').format(format)
      return imshow(a, format='jpeg')
    else:
      raise
  return disp

class Generator(object):

  def __init__(self, module_spec):
    self._module_spec = module_spec
    self._sess = None
    self._graph = tf.Graph()
    self._load_model()

  @property
  def z_dim(self):
    return self._z.shape[-1].value

  @property
  def conditional(self):
    return self._labels is not None

  def _load_model(self):
    with self._graph.as_default():
      self._generator = hub.Module(self._module_spec, name="gen_module",
                                   tags={"gen", "bsNone"})
      input_info = self._generator.get_input_info_dict()
      inputs = {k:...
Given the above code, generate some markdown instructions for it.
assistant
#Here is Python code to print ('Warning: image was too large to display in format "{}".

SSGAN Demo
This notebook is a demo of Generative Adversarial Networks (GANs) trained on ImageNet without labels using self-supervised techniques. Both generator and discrimantor models are available on TF Hub.
For more information about the models and the training procedure see our  paper [1]. 
The code for training these models is available on GitHub.
To get started, connect to a runtime and follow these steps:

(Optional) Select a model in the second code cell below.
Click Runtime > Run all to run each cell in order.
Afterwards, the interactive visualizations should update automatically when you modify the settings using the sliders and dropdown menus.

Note: if you run into any issues, youn can try restarting the runtime and rerunning all cells from scratch by clicking Runtime > Restart and run all....
[1] Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lucic, Neil Houlsby, Self-Supervised GANs via Auxiliary Rotation Loss, CVPR 2019.
Setup