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-# TensorFlow Style Guide
-
-This page contains style decisions that both developers and users of TensorFlow
-should follow to increase the readability of their code, reduce the number of
-errors, and promote consistency.
-
-[TOC]
-
-## Python style
-
-Generally follow
-[PEP8 Python style guide](https://www.python.org/dev/peps/pep-0008/),
-except for using 2 spaces.
-
-
-## Python 2 and 3 compatible
-
-* All code needs to be compatible with Python 2 and 3.
-
-* Next lines should be present in all Python files:
-
-```
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-```
-
-* Use `six` to write compatible code (for example `six.moves.range`).
-
-
-## Bazel BUILD rules
-
-TensorFlow uses Bazel build system and enforces next requirements:
-
-* Every BUILD file should contain next header:
-
-```
-# Description:
-#   <...>
-
-package(
-    default_visibility = ["//visibility:private"],
-)
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-```
-
-
-
-* For all Python BUILD targets (libraries and tests) add next line:
-
-```
-srcs_version = "PY2AND3",
-```
-
-
-## Tensor
-
-* Operations that deal with batches may assume that the first dimension of a Tensor is the batch dimension.
-
-* In most models the *last dimension* is the number of channels.
-
-* Dimensions excluding the first and last usually make up the "space" dimensions: Sequence-length or Image-size.
-
-## Python operations
-
-A *Python operation* is a function that, given input tensors and parameters,
-creates a part of the graph and returns output tensors.
-
-* The first arguments should be tensors, followed by basic python parameters.
- The last argument is `name` with a default value of `None`.
- If operation needs to save some `Tensor`s to Graph collections,
- put the arguments with names of the collections right before `name` argument.
-
-* Tensor arguments should be either a single tensor or an iterable of tensors.
- E.g. a "Tensor or list of Tensors" is too broad. See `assert_proper_iterable`.
-
-* Operations that take tensors as arguments should call `convert_to_tensor`
- to convert non-tensor inputs into tensors if they are using C++ operations.
- Note that the arguments are still described as a `Tensor` object
- of a specific dtype in the documentation.
-
-* Each Python operation should have a `name_scope` like below. Pass as
- arguments `name`, a default name of the op, and a list of the input tensors.
-
-* Operations should contain an extensive Python comment with Args and Returns
- declarations that explain both the type and meaning of each value. Possible
- shapes, dtypes, or ranks should be specified in the description.
- [See documentation details](../community/documentation.md)
-
-* For increased usability include an example of usage with inputs / outputs
- of the op in Example section.
-
-Example:
-
-    def my_op(tensor_in, other_tensor_in, my_param, other_param=0.5,
-              output_collections=(), name=None):
-      """My operation that adds two tensors with given coefficients.
-
-      Args:
-        tensor_in: `Tensor`, input tensor.
-        other_tensor_in: `Tensor`, same shape as `tensor_in`, other input tensor.
-        my_param: `float`, coefficient for `tensor_in`.
-        other_param: `float`, coefficient for `other_tensor_in`.
-        output_collections: `tuple` of `string`s, name of the collection to
-                            collect result of this op.
-        name: `string`, name of the operation.
-
-      Returns:
-        `Tensor` of same shape as `tensor_in`, sum of input values with coefficients.
-
-      Example:
-        >>> my_op([1., 2.], [3., 4.], my_param=0.5, other_param=0.6,
-                  output_collections=['MY_OPS'], name='add_t1t2')
-        [2.3, 3.4]
-      """
-      with tf.name_scope(name, "my_op", [tensor_in, other_tensor_in]):
-        tensor_in = tf.convert_to_tensor(tensor_in)
-        other_tensor_in = tf.convert_to_tensor(other_tensor_in)
-        result = my_param * tensor_in + other_param * other_tensor_in
-        tf.add_to_collection(output_collections, result)
-        return result
-
-Usage:
-
-    output = my_op(t1, t2, my_param=0.5, other_param=0.6,
-                   output_collections=['MY_OPS'], name='add_t1t2')
-
-
-## Layers
-
-Use `tf.keras.layers`, not `tf.layers`.
-
-See `tf.keras.layers` and [the Keras guide](../guide/keras.md#custom_layers) for details on how to sub-class layers.