Dynlink    = flag.Bool("dynlink", false, "support references to Go symbols defined in other shared libraries")
	Linkshared = flag.Bool("linkshared", false, "generate code that will be linked against Go shared libraries")
	AllErrors  = flag.Bool("e", false, "no limit on number of errors reported")

      "keras.api._v1.keras.__internal__.legacy.rnn_cell")
_current_module.nn.__path__ = [_module_dir] + _current_module.nn.__path__

del importlib

# Load all plugin libraries from site-packages/tensorflow-plugins if we are
# running under pip.
# TODO(gunan): Find a better location for this code snippet.
from tensorflow.python.framework import load_library as _ll

{
    "description": "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.",
    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }

	-S
		Print assembly and machine code.
	-V
		Print assembler version and exit.
	-debug
		Dump instructions as they are parsed.
	-dynlink
		Support references to Go symbols defined in other shared libraries.
	-e
		No limit on number of errors reported.
	-gensymabis
		Write symbol ABI information to output file. Don't assemble.
	-o file
		Write output to file. The default is foo.o for /a/b/c/foo.s.
	-p pkgpath

  exit 1
  ;;
esac

mkdir -p "${TARGET}"

# Download glibc's shared and development libraries based on the value of the
# `VERSION` parameter.
# Note: 'Templatizing' this and the other conditional branches would require
# defining several variables (version, os, path) making it difficult to maintain

This will catch most license, Python coding style and BUILD file issues that
may exist in your changes.

#### Running unit tests

There are two ways to run TensorFlow unit tests.

1.  Using tools and libraries installed directly on your system.

    Refer to the
    [CPU-only developer Dockerfile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/dockerfiles/dockerfiles/devel-cpu.Dockerfile)
    and

[TensorFlow](https://www.tensorflow.org/) is an end-to-end open source platform
for machine learning. It has a comprehensive, flexible ecosystem of
[tools](https://www.tensorflow.org/resources/tools),
[libraries](https://www.tensorflow.org/resources/libraries-extensions), and
[community](https://www.tensorflow.org/community) resources that lets
researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

# (e.g. _pywrap_tensorflow_internal.so for Python, libtensorflow.so for the C
# API) is responsible for registering ops with libtensorflow_framework.so. In
# addition to this core set of ops, user libraries which are loaded (via
# TF_LoadLibrary/tf.load_op_library) register their ops and kernels with this
# shared object directly.
#
# For example, from Python tf.load_op_library loads a custom op library (via

ln -s "/usr/include/asm-generic" "${TARGET}/usr/include/asm-generic"
ln -s "/usr/include/aarch64-linux-gnu/asm" "${TARGET}/usr/include/asm"

# Download glibc's shared and development libraries based on the value of the
# `VERSION` parameter.
# Note: 'Templatizing' this and the other conditional branches would require
# defining several variables (version, os, path) making it difficult to maintain

* Hermetic CUDA support is added.

  Hermetic CUDA uses a specific downloadable version of CUDA instead of the user’s locally installed CUDA. Bazel will download CUDA, CUDNN and NCCL distributions, and then use CUDA libraries and tools as dependencies in various Bazel targets. This enables more reproducible builds for Google ML projects and supported CUDA versions.