[pip.parse(
    extra_hub_aliases = {
        "numpy": ["numpy_headers"],
    },
    hub_name = "tf_pypi",
    python_version = python_version,
    requirements_lock = "//:requirements_lock_{}.txt".format(python_version.replace(".", "_")),
    whl_modifications = {
        "@pypi_mods//:numpy.json": "numpy",  # @pypi_mods is defined in XLA's MODULE.bazel
    },
) for python_version in [

#### *Try your first TensorFlow program*

```shell
$ python
```

```python
>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'
```

For more examples, see the
[TensorFlow Tutorials](https://www.tensorflow.org/tutorials/).

## Contribution guidelines

TF_CAPI_EXPORT extern void TFE_ContextEndStep(TFE_Context* ctx);

#ifdef __cplusplus
} /* end extern "C" */
#endif

#ifdef __cplusplus
// A workaround to ease conversion to and from numpy objects and
// TFE_TensorHandle's.
//
// TODO(ashankar): Figure out an alternative scheme that precludes the need for
// these API-boundary breaking methods.
namespace tensorflow {
class Tensor;