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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Overview
This colab demonstrates the steps to use the DeepLab model to perform semantic segmentation on a sample input image. Expected outputs are semantic labels overlayed on the sample image.
About DeepLab
The models used in this colab perform semantic segmentation. Semantic segmentation models focus on assigning semantic labels, such as sky, person, or car, to multiple objects and stuff in a single image.
Instructions
<h3><a href="https
Step7:  Import helper methods
These methods help us perform the following tasks
Step8:  Select a pretrained model
We have trained the DeepLab model using various backbone networks. Select one from the MODEL_NAME list.
Step10:  Run on sample images
Select one of sample images (leave IMAGE_URL empty) or feed any internet image
url for inference.
Note that this colab uses single scale inference for fast computation,
so the results may slightly differ from the visualizations in the 
README file,
which uses multi-scale and left-right flipped inputs.
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Python Code:
import os
from io import BytesIO
import tarfile
import tempfile
from six.moves import urllib
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
from PIL import Image
import tensorflow as tf
Explanation: Overview
This colab demonstrates the steps to use the DeepLab model to perform semantic segmentation on a sample input image. Expected outputs are semantic labels overlayed on the sample image.
About DeepLab
The models used in this colab perform semantic segmentation. Semantic segmentation models focus on assigning semantic labels, such as sky, person, or car, to multiple objects and stuff in a single image.
Instructions
<h3><a href="https://cloud.google.com/tpu/"><img valign="middle" src="https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/tpu-hexagon.png" width="50"></a>  &nbsp;&nbsp;Use a free TPU device</h3>
On the main menu, click Runtime and select Change runtime type. Set "TPU" as the hardware accelerator.
Click Runtime again and select Runtime > Run All. You can also run the cells manually with Shift-ENTER.
Import Libraries
End of explanation
class DeepLabModel(object):
  Class to load deeplab model and run inference.
  INPUT_TENSOR_NAME = 'ImageTensor:0'
  OUTPUT_TENSOR_NAME = 'SemanticPredictions:0'
  INPUT_SIZE = 513
  FROZEN_GRAPH_NAME = 'frozen_inference_graph'
  def __init__(self, tarball_path):
    Creates and loads pretrained deeplab model.
    self.graph = tf.Graph()
    graph_def = None
    # Extract frozen graph from tar archive.
    tar_file = tarfile.open(tarball_path)
    for tar_info in tar_file.getmembers():
      if self.FROZEN_GRAPH_NAME in os.path.basename(tar_info.name):
        file_handle = tar_file.extractfile(tar_info)
        graph_def = tf.GraphDef.FromString(file_handle.read())
        break
    tar_file.close()
    if graph_def is None:
      raise RuntimeError('Cannot find inference graph in tar archive.')
    with self.graph.as_default():
      tf.import_graph_def(graph_def, name='')
    self.sess = tf.Session(graph=self.graph)
  def run(self, image):
    Runs inference on a single image.
    Args:
      image: A PIL.Image object, raw input image.
    Returns:
      resized_image: RGB image resized from original input image.
      seg_map: Segmentation map of `resized_image`.
    
    width, height = image.size
    resize_ratio = 1.0 * self.INPUT_SIZE / max(width, height)
    target_size = (int(resize_ratio * width), int(resize_ratio * height))
    resized_image = image.convert('RGB').resize(target_size, Image.ANTIALIAS)
    batch_seg_map = self.sess.run(
        self.OUTPUT_TENSOR_NAME,
        feed_dict={self.INPUT_TENSOR_NAME: [np.asarray(resized_image)]})
    seg_map = batch_seg_map[0]
    return resized_image, seg_map
def create_pascal_label_colormap():
  Creates a label colormap used in PASCAL VOC segmentation benchmark.
  Returns:
    A Colormap for visualizing segmentation results.
  
  colormap = np.zeros((256, 3), dtype=int)
  ind = np.arange(256, dtype=int)
  for shift in reversed(range(8)):
    for channel in range(3):
      colormap[:, channel] |= ((ind >> channel) & 1) << shift
    ind >>= 3
  return colormap
def label_to_color_image(label):
  Adds color defined by the dataset colormap to the label.
  Args:
    label: A 2D array with integer type, storing the segmentation label.
  Returns:
    result: A 2D array with floating type. The element of the array
      is the color indexed by the corresponding element in the input label
      to the PASCAL color map.
  Raises:
    ValueError: If label is not of rank 2 or its value is larger than color
      map maximum entry.
  
  if label.ndim != 2:
    raise ValueError('Expect 2-D input label')
  colormap = create_pascal_label_colormap()
  if np.max(label) >= len(colormap):
    raise ValueError('label value too large.')
  return colormap[label]
def vis_segmentation(image, seg_map):
  Visualizes input image, segmentation map and overlay view.
  plt.figure(figsize=(15, 5))
  grid_spec = gridspec.GridSpec(1, 4, width_ratios=[6, 6, 6, 1])
  plt.subplot(grid_spec[0])
  plt.imshow(image)
  plt.axis('off')
  plt.title('input image')
  plt.subplot(grid_spec[1])
  seg_image = label_to_color_image(seg_map).astype(np.uint8)
  plt.imshow(seg_image)
  plt.axis('off')
  plt.title('segmentation map')
  plt.subplot(grid_spec[2])
  plt.imshow(image)
  plt.imshow(seg_image, alpha=0.7)
  plt.axis('off')
  plt.title('segmentation overlay')
  unique_labels = np.unique(seg_map)
  ax = plt.subplot(grid_spec[3])
  plt.imshow(
      FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation='nearest')
  ax.yaxis.tick_right()
  plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
  plt.xticks([], [])
  ax.tick_params(width=0.0)
  plt.grid('off')
  plt.show()
LABEL_NAMES = np.asarray([
    'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
    'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
    'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tv'
])
FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
Explanation: Import helper methods
These methods help us perform the following tasks:
* Load the latest version of the pretrained DeepLab model
* Load the colormap from the PASCAL VOC dataset
* Adds colors to various labels, such as "pink" for people, "green" for bicycle and more
* Visualize an image, and add an overlay of colors on various regions
End of explanation
MODEL_NAME = 'mobilenetv2_coco_voctrainaug'  # @param ['mobilenetv2_coco_voctrainaug', 'mobilenetv2_coco_voctrainval', 'xception_coco_voctrainaug', 'xception_coco_voctrainval']
_DOWNLOAD_URL_PREFIX = 'http://download.tensorflow.org/models/'
_MODEL_URLS = {
    'mobilenetv2_coco_voctrainaug':
        'deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz',
    'mobilenetv2_coco_voctrainval':
        'deeplabv3_mnv2_pascal_trainval_2018_01_29.tar.gz',
    'xception_coco_voctrainaug':
        'deeplabv3_pascal_train_aug_2018_01_04.tar.gz',
    'xception_coco_voctrainval':
        'deeplabv3_pascal_trainval_2018_01_04.tar.gz',
}
_TARBALL_NAME = 'deeplab_model.tar.gz'
model_dir = tempfile.mkdtemp()
tf.gfile.MakeDirs(model_dir)
download_path = os.path.join(model_dir, _TARBALL_NAME)
print('downloading model, this might take a while...')
urllib.request.urlretrieve(_DOWNLOAD_URL_PREFIX + _MODEL_URLS[MODEL_NAME],
                   download_path)
print('download completed! loading DeepLab model...')
MODEL = DeepLabModel(download_path)
print('model loaded successfully!')
Explanation: Select a pretrained model
We have trained the DeepLab model using various backbone networks. Select one from the MODEL_NAME list.
End of explanation
SAMPLE_IMAGE = 'image1'  # @param ['image1', 'image2', 'image3']
IMAGE_URL = ''  #@param {type:"string"}
_SAMPLE_URL = ('https://github.com/tensorflow/models/blob/master/research/'
               'deeplab/g3doc/img/%s.jpg?raw=true')
def run_visualization(url):
  Inferences DeepLab model and visualizes result.
  try:
    f = urllib.request.urlopen(url)
    jpeg_str = f.read()
    original_im = Image.open(BytesIO(jpeg_str))
  except IOError:
    print('Cannot retrieve image. Please check url: ' + url)
    return
  print('running deeplab on image %s...' % url)
  resized_im, seg_map = MODEL.run(original_im)
  vis_segmentation(resized_im, seg_map)
image_url = IMAGE_URL or _SAMPLE_URL % SAMPLE_IMAGE
run_visualization(image_url)
Explanation: Run on sample images
Select one of sample images (leave IMAGE_URL empty) or feed any internet image
url for inference.
Note that this colab uses single scale inference for fast computation,
so the results may slightly differ from the visualizations in the 
README file,
which uses multi-scale and left-right flipped inputs.
End of explanation
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