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-# Constants, Sequences, and Random Values
-
-Note: Functions taking `Tensor` arguments can also take anything accepted by
-`tf.convert_to_tensor`.
-
-[TOC]
-
-## Constant Value Tensors
-
-TensorFlow provides several operations that you can use to generate constants.
-
-*   `tf.zeros`
-*   `tf.zeros_like`
-*   `tf.ones`
-*   `tf.ones_like`
-*   `tf.fill`
-*   `tf.constant`
-
-## Sequences
-
-*   `tf.linspace`
-*   `tf.range`
-
-## Random Tensors
-
-TensorFlow has several ops that create random tensors with different
-distributions.  The random ops are stateful, and create new random values each
-time they are evaluated.
-
-The `seed` keyword argument in these functions acts in conjunction with
-the graph-level random seed. Changing either the graph-level seed using
-`tf.set_random_seed` or the
-op-level seed will change the underlying seed of these operations. Setting
-neither graph-level nor op-level seed, results in a random seed for all
-operations.
-See `tf.set_random_seed`
-for details on the interaction between operation-level and graph-level random
-seeds.
-
-### Examples:
-
-```python
-# Create a tensor of shape [2, 3] consisting of random normal values, with mean
-# -1 and standard deviation 4.
-norm = tf.random_normal([2, 3], mean=-1, stddev=4)
-
-# Shuffle the first dimension of a tensor
-c = tf.constant([[1, 2], [3, 4], [5, 6]])
-shuff = tf.random_shuffle(c)
-
-# Each time we run these ops, different results are generated
-sess = tf.Session()
-print(sess.run(norm))
-print(sess.run(norm))
-
-# Set an op-level seed to generate repeatable sequences across sessions.
-norm = tf.random_normal([2, 3], seed=1234)
-sess = tf.Session()
-print(sess.run(norm))
-print(sess.run(norm))
-sess = tf.Session()
-print(sess.run(norm))
-print(sess.run(norm))
-```
-
-Another common use of random values is the initialization of variables. Also see
-the [Variables How To](../../guide/variables.md).
-
-```python
-# Use random uniform values in [0, 1) as the initializer for a variable of shape
-# [2, 3]. The default type is float32.
-var = tf.Variable(tf.random_uniform([2, 3]), name="var")
-init = tf.global_variables_initializer()
-
-sess = tf.Session()
-sess.run(init)
-print(sess.run(var))
-```
-
-*   `tf.random_normal`
-*   `tf.truncated_normal`
-*   `tf.random_uniform`
-*   `tf.random_shuffle`
-*   `tf.random_crop`
-*   `tf.multinomial`
-*   `tf.random_gamma`
-*   `tf.set_random_seed`