from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader

# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.
_os.environ["TF2_BEHAVIOR"] = "1"
from tensorflow.python import tf2 as _tf2
_tf2.enable()

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

# Make sure directory containing top level submodules is in

# Options to build TensorFlow 1.x or 2.x.
# TODO(kanglan): Change v2's define to default behavior
build:v2 --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1

# Enable all targets in XLA
build:cpu_cross --define=with_cross_compiler_support=true

# Disable XLA on mobile.
build:xla     --define=with_xla_support=true # TODO: remove, it's on by default.

*   `tf.lite`
    *   Keras-based LSTM models must be converted with an explicit batch size in
        the input layer.

## Bug Fixes and Other Changes

### TF Core:

*   Set `tf2_behavior` to 1 to enable V2 for early loading cases.
*   Add `execute_fn_for_device function` to dynamically choose the
    implementation based on underlying device placement.
*   Eager: