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Point v1 links at 1.15 tags instead of master since files move.
Remove tensorfow.org/code links. PiperOrigin-RevId: 602703415
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site/en/community/contribute/docs.md

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@@ -32,7 +32,7 @@ To participate in the TensorFlow docs community:
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For details, use the [TensorFlow API docs contributor guide](docs_ref.md). This
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shows you how to find the
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[source file](https://www.tensorflow.org/code/tensorflow/python/)
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[source file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/)
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and edit the symbol's
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<a href="https://www.python.org/dev/peps/pep-0257/" class="external">docstring</a>.
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Many API reference pages on tensorflow.org include a link to the source file
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<a href="https://github.com/tensorflow/tensorflow" class="external">tensorflow/tensorflow</a>
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repo. The reference documentation is generated from code comments
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and docstrings in the source code for
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<a href="https://www.tensorflow.org/code/tensorflow/python/" class="external">Python</a>,
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<a href="https://www.tensorflow.org/code/tensorflow/cc/" class="external">C++</a>, and
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<a href="https://www.tensorflow.org/code/tensorflow/java/" class="external">Java</a>.
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<a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/" class="external">Python</a>,
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<a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/" class="external">C++</a>, and
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<a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/" class="external">Java</a>.
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Previous versions of the TensorFlow documentation are available as
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[rX.x branches](https://github.com/tensorflow/docs/branches) in the TensorFlow

site/en/community/contribute/docs_ref.md

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@@ -8,7 +8,7 @@ TensorFlow uses [DocTest](https://docs.python.org/3/library/doctest.html) to
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test code snippets in Python docstrings. The snippet must be executable Python
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code. To enable testing, prepend the line with `>>>` (three left-angle
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brackets). For example, here's a excerpt from the `tf.concat` function in the
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[array_ops.py](https://www.tensorflow.org/code/tensorflow/python/ops/array_ops.py)
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[array_ops.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/array_ops.py)
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source file:
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```
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* If you are only changing the docstring of a class/function/method, then you
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can test it by passing that file's path to
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[tf_doctest.py](https://www.tensorflow.org/code/tensorflow/tools/docs/tf_doctest.py).
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[tf_doctest.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docs/tf_doctest.py).
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For example:
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<pre class="prettyprint lang-bsh">

site/en/guide/create_op.md

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@@ -152,17 +152,17 @@ REGISTER_KERNEL_BUILDER(Name("ZeroOut").Device(DEVICE_CPU), ZeroOutOp);
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> Important: Instances of your OpKernel may be accessed concurrently.
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> Your `Compute` method must be thread-safe. Guard any access to class
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> members with a mutex. Or better yet, don't share state via class members!
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> Consider using a [`ResourceMgr`](https://www.tensorflow.org/code/tensorflow/core/framework/resource_mgr.h)
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> Consider using a [`ResourceMgr`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/resource_mgr.h)
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> to keep track of op state.
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### Multi-threaded CPU kernels
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To write a multi-threaded CPU kernel, the Shard function in
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[`work_sharder.h`](https://www.tensorflow.org/code/tensorflow/core/util/work_sharder.h)
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[`work_sharder.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/util/work_sharder.h)
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can be used. This function shards a computation function across the
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threads configured to be used for intra-op threading (see
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intra_op_parallelism_threads in
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[`config.proto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto)).
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[`config.proto`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/config.proto)).
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### GPU kernels
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* The `context`, which can either be an `OpKernelContext` or
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`OpKernelConstruction` pointer (see
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[`tensorflow/core/framework/op_kernel.h`](https://www.tensorflow.org/code/tensorflow/core/framework/op_kernel.h)),
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[`tensorflow/core/framework/op_kernel.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_kernel.h)),
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for its `SetStatus()` method.
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* The condition. For example, there are functions for validating the shape
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of a tensor in
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[`tensorflow/core/framework/tensor_shape.h`](https://www.tensorflow.org/code/tensorflow/core/framework/tensor_shape.h)
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[`tensorflow/core/framework/tensor_shape.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.h)
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* The error itself, which is represented by a `Status` object, see
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[`tensorflow/core/platform/status.h`](https://www.tensorflow.org/code/tensorflow/core/platform/status.h). A
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[`tensorflow/core/platform/status.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/status.h). A
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`Status` has both a type (frequently `InvalidArgument`, but see the list of
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types) and a message. Functions for constructing an error may be found in
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[`tensorflow/core/platform/errors.h`][validation-macros].
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The specific lists of types allowed by these are defined by the functions (like
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`NumberTypes()`) in
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[`tensorflow/core/framework/types.h`](https://www.tensorflow.org/code/tensorflow/core/framework/types.h).
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[`tensorflow/core/framework/types.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.h).
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In this example the attr `t` must be one of the numeric types:
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```c++
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type into a list of varying types).
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The full list of safe and unsafe changes can be found in
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[`tensorflow/core/framework/op_compatibility_test.cc`](https://www.tensorflow.org/code/tensorflow/core/framework/op_compatibility_test.cc).
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[`tensorflow/core/framework/op_compatibility_test.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_compatibility_test.cc).
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If you cannot make your change to an operation backwards compatible, then create
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a new operation with a new name with the new semantics.
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You can implement different OpKernels and register one for CPU and another for
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GPU, just like you can [register kernels for different types](#polymorphism).
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There are several examples of kernels with GPU support in
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[`tensorflow/core/kernels/`](https://www.tensorflow.org/code/tensorflow/core/kernels/).
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[`tensorflow/core/kernels/`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/).
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Notice some kernels have a CPU version in a `.cc` file, a GPU version in a file
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ending in `_gpu.cu.cc`, and some code shared in common in a `.h` file.
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For example, the `tf.pad` has
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everything but the GPU kernel in [`tensorflow/core/kernels/pad_op.cc`][pad_op].
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The GPU kernel is in
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[`tensorflow/core/kernels/pad_op_gpu.cu.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op_gpu.cu.cc),
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[`tensorflow/core/kernels/pad_op_gpu.cu.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/pad_op_gpu.cu.cc),
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and the shared code is a templated class defined in
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[`tensorflow/core/kernels/pad_op.h`](https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op.h).
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[`tensorflow/core/kernels/pad_op.h`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/pad_op.h).
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We organize the code this way for two reasons: it allows you to share common
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code among the CPU and GPU implementations, and it puts the GPU implementation
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into a separate file so that it can be compiled only by the GPU compiler.
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#### Compiling the kernel for the GPU device
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Look at
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[cuda_op_kernel.cu.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
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[cuda_op_kernel.cu.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
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for an example that uses a CUDA kernel to implement an op. The
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`tf_custom_op_library` accepts a `gpu_srcs` argument in which the list of source
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files containing the CUDA kernels (`*.cu.cc` files) can be specified. For use
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with a binary installation of TensorFlow, the CUDA kernels have to be compiled
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with NVIDIA's `nvcc` compiler. Here is the sequence of commands you can use to
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compile the
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[cuda_op_kernel.cu.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
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[cuda_op_kernel.cu.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/adding_an_op/cuda_op_kernel.cu.cc)
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and
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[cuda_op_kernel.cc](https://www.tensorflow.org/code/tensorflow/examples/adding_an_op/cuda_op_kernel.cc)
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[cuda_op_kernel.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/adding_an_op/cuda_op_kernel.cc)
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into a single dynamically loadable library:
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```bash
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There are a number of common shape functions
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that apply to many ops, such as `shape_inference::UnchangedShape` which can be
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found in [common_shape_fns.h](https://www.tensorflow.org/code/tensorflow/core/framework/common_shape_fns.h) and used as follows:
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found in [common_shape_fns.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/common_shape_fns.h) and used as follows:
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```c++
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REGISTER_OP("ZeroOut")
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Since shape inference is an optional feature, and the shapes of tensors may vary
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dynamically, shape functions must be robust to incomplete shape information for
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any of the inputs. The `Merge` method in [`InferenceContext`](https://www.tensorflow.org/code/tensorflow/core/framework/shape_inference.h)
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any of the inputs. The `Merge` method in [`InferenceContext`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/shape_inference.h)
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allows the caller to assert that two shapes are the same, even if either
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or both of them do not have complete information. Shape functions are defined
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for all of the core TensorFlow ops and provide many different usage examples.
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validating that various input shape combinations produce the expected output
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shape combinations. You can see examples of how to write these tests in some
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our
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[core ops tests](https://www.tensorflow.org/code/tensorflow/core/ops/array_ops_test.cc).
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[core ops tests](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/array_ops_test.cc).
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(The syntax of `INFER_OK` and `INFER_ERROR` are a little cryptic, but try to be
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compact in representing input and output shape specifications in tests. For
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now, see the surrounding comments in those tests to get a sense of the shape
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guide shows how to build custom ops from the TensorFlow pip package instead
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[core-array_ops]:https://www.tensorflow.org/code/tensorflow/core/ops/array_ops.cc
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[python-user_ops]:https://www.tensorflow.org/code/tensorflow/python/user_ops/user_ops.py
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[tf-kernels]:https://www.tensorflow.org/code/tensorflow/core/kernels/
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[user_ops]:https://www.tensorflow.org/code/tensorflow/core/user_ops/
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[pad_op]:https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op.cc
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[standard_ops-py]:https://www.tensorflow.org/code/tensorflow/python/ops/standard_ops.py
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[standard_ops-cc]:https://www.tensorflow.org/code/tensorflow/cc/ops/standard_ops.h
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[python-BUILD]:https://www.tensorflow.org/code/tensorflow/python/BUILD
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[validation-macros]:https://www.tensorflow.org/code/tensorflow/core/platform/errors.h
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[op_def_builder]:https://www.tensorflow.org/code/tensorflow/core/framework/op_def_builder.h
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[register_types]:https://www.tensorflow.org/code/tensorflow/core/framework/register_types.h
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[FinalizeAttr]:https://www.tensorflow.org/code/tensorflow/core/framework/op_def_builder.cc
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[DataTypeString]:https://www.tensorflow.org/code/tensorflow/core/framework/types.cc
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[python-BUILD]:https://www.tensorflow.org/code/tensorflow/python/BUILD
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[types-proto]:https://www.tensorflow.org/code/tensorflow/core/framework/types.proto
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[TensorShapeProto]:https://www.tensorflow.org/code/tensorflow/core/framework/tensor_shape.proto
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[TensorProto]:https://www.tensorflow.org/code/tensorflow/core/framework/tensor.proto
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[core-array_ops]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/array_ops.cc
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[python-user_ops]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/user_ops/user_ops.py
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[tf-kernels]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/
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[user_ops]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/user_ops/
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[pad_op]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/pad_op.cc
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[standard_ops-py]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/standard_ops.py
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[standard_ops-cc]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/ops/standard_ops.h
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[python-BUILD]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/BUILD
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[validation-macros]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/errors.h
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[op_def_builder]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def_builder.h
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[register_types]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/register_types.h
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[FinalizeAttr]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op_def_builder.cc
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[DataTypeString]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.cc
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[python-BUILD]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/BUILD
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[types-proto]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.proto
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[TensorShapeProto]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto
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[TensorProto]:https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto

site/en/guide/versions.md

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1. Add a new similar op named `SomethingV2` or similar and go through the
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To ensure forward compatibility use the checks suggested in
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[compat.py](https://www.tensorflow.org/code/tensorflow/python/compat/compat.py)
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[compat.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/compat/compat.py)
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when changing the Python wrappers.
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2. Remove the old op (Can only take place with a major version change due to
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site/en/r1/guide/autograph.ipynb

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"id": "CydFK2CL7ZHA"
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},
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"source": [
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"[AutoGraph](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/) helps you write complicated graph code using normal Python. Behind the scenes, AutoGraph automatically transforms your code into the equivalent [TensorFlow graph code](https://www.tensorflow.org/r1/guide/graphs). AutoGraph already supports much of the Python language, and that coverage continues to grow. For a list of supported Python language features, see the [Autograph capabilities and limitations](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/g3doc/reference/limitations.md)."
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"[AutoGraph](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/autograph/) helps you write complicated graph code using normal Python. Behind the scenes, AutoGraph automatically transforms your code into the equivalent [TensorFlow graph code](https://www.tensorflow.org/r1/guide/graphs). AutoGraph already supports much of the Python language, and that coverage continues to grow. For a list of supported Python language features, see the [Autograph capabilities and limitations](https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow/python/autograph/g3doc/reference/limitations.md)."
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{

site/en/r1/guide/custom_estimators.md

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* The
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[official TensorFlow implementation of MNIST](https://github.com/tensorflow/models/tree/master/official/r1/mnist),
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[official TensorFlow implementation of MNIST](https://github.com/tensorflow/models/tree/r1.15/official/r1/mnist),
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which uses a custom estimator.
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* The TensorFlow
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[official models repository](https://github.com/tensorflow/models/tree/master/official),
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[official models repository](https://github.com/tensorflow/models/tree/r1.15/official),
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which contains more curated examples using custom estimators.
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* This [TensorBoard video](https://youtu.be/eBbEDRsCmv4), which introduces
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TensorBoard.

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