message: Update get_started.md
  * Update get_started.md
  Add python 2 compatibility for tutorial code
  * Update get_started.md
  Corrected console outputs
  Included or rearrange some import statements
diff: @@ -104,6 +104,7 @@ operations (Operations are also nodes). For example, we can add our two
  constant nodes and produce a new graph as follows:
  ```python
  +from __future__ import print_function
  node3 = tf.add(node1, node2)
  print("node3:", node3)
  print("sess.run(node3):", sess.run(node3))
  @@ -279,15 +280,14 @@ print(sess.run([W, b]))
  ```
  results in the final model parameters:
  ```
  -[array([-0.9999969], dtype=float32), array([ 0.99999082],
  - dtype=float32)]
  +[array([-0.9999969], dtype=float32), array([ 0.99999082], dtype=float32)]
  ```
  -Now we have done actual machine learning! Although doing this simple linear
  -regression doesn't require much TensorFlow core code, more complicated models
  -and methods to feed data into your model necessitate more code. Thus TensorFlow
  -provides higher level abstractions for common patterns, structures, and
  -functionality. We will learn how to use some of these abstractions in the
  +Now we have done actual machine learning! Although this simple linear
  +regression model does not require much TensorFlow core code, more complicated
  +models and methods to feed data into your models necessitate more code. Thus,
  +TensorFlow provides higher level abstractions for common patterns, structures,
  +and functionality. We will learn how to use some of these abstractions in the
  next section.
  ### Complete program
  @@ -354,9 +354,9 @@ Notice how much simpler the linear regression program becomes with
  `tf.estimator`:
  ```python
  -import tensorflow as tf
  # NumPy is often used to load, manipulate and preprocess data.
  import numpy as np
  +import tensorflow as tf
  # Declare list of features. We only have one numeric feature. There are many
  # other types of columns that are more complicated and useful.
  @@ -393,10 +393,10 @@ eval_metrics = estimator.evaluate(input_fn=eval_input_fn)
  print("train metrics: %r"% train_metrics)
  print("eval metrics: %r"% eval_metrics)
  ```
  -When run, it produces
  +When run, it produces something like
  ```
  -train metrics: {'loss': 1.2712867e-09, 'global_step': 1000}
  -eval metrics: {'loss': 0.0025279333, 'global_step': 1000}
  +train metrics: {'average_loss': 1.4833182e-08, 'global_step': 1000, 'loss': 5.9332727e-08}
  +eval metrics: {'average_loss': 0.0025353201, 'global_step': 1000, 'loss': 0.01014128}
  ```
  Notice how our eval data has a higher loss, but it is still close to zero.
  That means we are learning properly.
  