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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:
 Copyright 2017 Google LLC.
Step1:  TensorFlow Programming Concepts
Learning Objectives
Step2:  Don't forget to execute the preceding code block (the import statements).
Other common import statements include the following
Step3:  Exercise
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Python Code:
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
Explanation: Copyright 2017 Google LLC.
End of explanation
import tensorflow as tf
Explanation: TensorFlow Programming Concepts
Learning Objectives:
  * Learn the basics of the TensorFlow programming model, focusing on the following concepts:
    * tensors
    * operations
    * graphs
    * sessions
  * Build a simple TensorFlow program that creates a default graph, and a session that runs the graph
Note: Please read through this tutorial carefully. The TensorFlow programming model is probably different from others that you have encountered, and thus may not be as intuitive as you'd expect.
Overview of Concepts
TensorFlow gets its name from tensors, which are arrays of arbitrary dimensionality. Using TensorFlow, you can manipulate tensors with a very high number of dimensions. That said, most of the time you will work with one or more of the following low-dimensional tensors:
A scalar is a 0-d array (a 0th-order tensor).  For example, "Howdy" or 5
A vector is a 1-d array (a 1st-order tensor).  For example, [2, 3, 5, 7, 11] or [5]
A matrix is a 2-d array (a 2nd-order tensor).  For example, [[3.1, 8.2, 5.9][4.3, -2.7, 6.5]]
TensorFlow operations create, destroy, and manipulate tensors.  Most of the lines of code in a typical TensorFlow program are operations.
A TensorFlow graph (also known as a computational graph or a dataflow graph) is, yes, a graph data structure.  A graph's nodes are operations (in TensorFlow, every operation is associated with a graph).  Many TensorFlow programs consist of a single graph, but TensorFlow programs may optionally create multiple graphs. A graph's nodes are operations; a graph's edges are tensors. Tensors flow through the graph, manipulated at each node by an operation. The output tensor of one operation often becomes the input tensor to a subsequent operation. TensorFlow implements a lazy execution model, meaning that nodes are only computed when needed, based on the needs of associated nodes.
Tensors can be stored in the graph as constants or variables. As you might guess, constants hold tensors whose values can't change, while variables hold tensors whose values can change. However, what you may not have guessed is that constants and variables are just more operations in the graph. A constant is an operation that always returns the same tensor value. A variable is an operation that will return whichever tensor has been assigned to it.
To define a constant, use the tf.constant operator and pass in its value. For example:
x = tf.constant(5.2)
Similarly, you can create a variable like this:
y = tf.Variable([5])
Or you can create the variable first and then subsequently assign a value like this (note that you always have to specify a default value):
y = tf.Variable([0])
  y = y.assign([5])
Once you've defined some constants or variables, you can combine them with other operations like tf.add. When you evaluate the tf.add operation, it will call your tf.constant or tf.Variable operations to get their values and then return a new tensor with the sum of those values.
Graphs must run within a TensorFlow session, which holds the state for the graph(s) it runs:
with tf.Session() as sess:
  initialization = tf.global_variables_initializer()
  print(y.eval())
When working with tf.Variables, you must explicitly initialize them by calling tf.global_variables_initializer at the start of your session, as shown above.
Note: A session can distribute graph execution across multiple machines (assuming the program is run on some distributed computation framework). For more information, see Distributed TensorFlow.
Summary
TensorFlow programming is essentially a two-step process:
Assemble constants, variables, and operations into a graph.
Evaluate those constants, variables and operations within a session.
Creating a Simple TensorFlow Program
Let's look at how to code a simple TensorFlow program that adds two constants. 
Provide import statements
As with nearly all Python programs, you'll begin by specifying some import statements.
The set of import statements required to run a TensorFlow program depends, of course, on the features your program will access. At a minimum, you must provide the import tensorflow statement in all TensorFlow programs:
End of explanation
from __future__ import print_function
import tensorflow as tf
# Create a graph.
g = tf.Graph()
# Establish the graph as the "default" graph.
with g.as_default():
  # Assemble a graph consisting of the following three operations:
  #   * Two tf.constant operations to create the operands.
  #   * One tf.add operation to add the two operands.
  x = tf.constant(8, name="x_const")
  y = tf.constant(5, name="y_const")
  my_sum = tf.add(x, y, name="x_y_sum")
  # Now create a session.
  # The session will run the default graph.
  with tf.Session() as sess:
    print(my_sum.eval())
Explanation: Don't forget to execute the preceding code block (the import statements).
Other common import statements include the following:
import matplotlib.pyplot as plt # Dataset visualization.
  import numpy as np              # Low-level numerical Python library.
  import pandas as pd             # Higher-level numerical Python library.
TensorFlow provides a default graph. However, we recommend explicitly creating your own Graph instead to facilitate tracking state (e.g., you may wish to work with a different Graph in each cell).
End of explanation
# Create a graph.
g = tf.Graph()
# Establish our graph as the "default" graph.
with g.as_default():
  # Assemble a graph consisting of three operations. 
  # (Creating a tensor is an operation.)
  x = tf.constant(8, name="x_const")
  y = tf.constant(5, name="y_const")
  my_sum = tf.add(x, y, name="x_y_sum")
  
  # Task 1: Define a third scalar integer constant z.
  z = tf.constant(4, name="z_const")
  # Task 2: Add z to `my_sum` to yield a new sum.
  new_sum = tf.add(my_sum, z, name="x_y_z_sum")
  # Now create a session.
  # The session will run the default graph.
  with tf.Session() as sess:
    # Task 3: Ensure the program yields the correct grand total.
    print(new_sum.eval())
Explanation: Exercise: Introduce a Third Operand
Revise the above code listing to add three integers, instead of two:
Define a third scalar integer constant, z, and assign it a value of 4.
Add z to my_sum to yield a new sum.
Hint: See the API docs for tf.add() for more details on its function signature.
Re-run the modified code block. Did the program generate the correct grand total?
Solution
Click below for the solution.
End of explanation
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