To install the current release, which includes support for
[CUDA-enabled GPU cards](https://www.tensorflow.org/install/gpu) *(Ubuntu and
Windows)*:

```
$ pip install tensorflow
```

Other devices (DirectX and MacOS-metal) are supported using
[Device plugins](https://www.tensorflow.org/install/gpu_plugins#available_devices).

A smaller CPU-only package is also available:

```
$ pip install tensorflow-cpu
```

The `_api/v2/api_packages.txt` file is generated during the TensorFlow API v2
init files creation process and is subsequently stored in the wheel file after
the build. It also contains a few paths that cannot be directly imported. These
paths point to attributes or sub-modules within a module's namespace, but they
don't correspond to an actual file or directory on the filesystem. The list of

#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/util/command_line_flags.h"

#if defined(PLATFORM_WINDOWS)
// Make mkdir resolve to _mkdir to create the test temporary directory.
#include <direct.h>
#define mkdir(name, mode) _mkdir(name)

// Windows defines the following macros to convert foo to fooA or fooW,
// depending on the type of the string argument. We don't use these macros, so
// undefine them here.

  close(src_fd);
  close(dst_fd);
  if (error_code != 0) {
    errno = error_code;
    return -1;
  } else {
    return 0;
  }
}

int RemoveSpecialDirectoryEntries(const struct dirent* entry) {
  return strcmp(entry->d_name, ".") != 0 && strcmp(entry->d_name, "..") != 0;
}

//     while (ReadDataObject(fp, &d)) {
//       if (d.IsBad()) {
//         LOG(ERROR) << "Bad Data";
//         return;
//       }
//       PushGoodData(d);
//     }
//   }
//
// You can use Cleanup<F> directly, instead of using MakeCleanup and auto,
// but there's rarely a reason to do that.
//
// You can call 'release()' on a Cleanup object to cancel the cleanup.

If you are experiencing or witnessing conflict, we ask you to use the following escalation strategy to address the conflict:

1.  Address the perceived conflict directly with those involved, preferably in a
    real-time medium.
2.  If this fails, get a third party (e.g. a mutual friend, and/or someone with
    background on the issue, but not involved in the conflict) to intercede.

  // Release any underlying resources, including the interface object.
  //
  // WARNING: The destructor of this class is marked as protected to disallow
  // clients from directly destroying this object since it may manage it's own
  // lifetime through ref counting. Thus this must be allocated on the heap and
  // clients MUST call Release() in order to destroy an instance of this class.

Here are the affected tests:
EOF
    while read dep; do
      echo "For dependency $dep:"
      # For every missing dependency, find the tests which directly depend on
      # it, and print that list for debugging. Not really clear if this is
      # helpful since the only examples I've seen are enormous.
      bazel query "rdeps(kind(py_test, $(cat $BATS_TEST_TMPDIR/deps)), $dep, 1)"

Status LoadDynamicLibrary(const char* library_filename, void** result,
                          const void** buf, size_t* len);

// TODO(josh11b,mrry): Change Session to be able to use a Graph*
// directly, instead of requiring us to serialize to a GraphDef and
// call Session::Extend().
bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status) {
  if (session->graph != nullptr) {

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