)

config_setting(
    name = "no_tensorflow_py_deps",
    define_values = {"no_tensorflow_py_deps": "true"},
    visibility = ["//visibility:public"],
)

# Sometimes Bazel reports darwin_x86_64 as "darwin" and sometimes as
# "darwin_x86_64". The former shows up when building on a Mac x86_64 host for a Mac x86_64 target.

    //     and places the 'read0_maybe_with_graph_error' op on task:2
    //   * task:0 partitions the main function with a subgraph containing
    //     'read0_maybe_with_graph_error' sent to task:2
    //   * task:2 graph pass reports an error when it sees
    //     'read0_maybe_with_graph_error' with dev2_name
    tensorflow::function_optimization_registration::
        FunctionOptimizationPassRegistration register_test_pass(

# 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
# the __path__ so that "from tensorflow.foo import bar" works.
# We're using bitwise, but there's nothing special about that.

    `TFCI` env param pointing to a list of `env` files.
2.  `utilities/setup.sh`, initialized by all top-level scripts, reads and sets
    values from those `TFCI` paths.
    -   `set -a` / `set -o allexport` exports the variables from `env` files so
        all scripts can use them.
    -   `utilities/setup_docker.sh` creates a container called `tf` with all
        `TFCI_` variables shared to it.

TF_CAPI_EXPORT extern void TFE_DeleteConfigKeyValue(TFE_Context* ctx,
                                                    const char* key,
                                                    TF_Status* status);

// Report error (specified by error_code and error_message) to other tasks in
// the cluster.
TF_CAPI_EXPORT extern void TFE_ReportErrorToCluster(TFE_Context* ctx,
                                                    int error_code,

from tensorflow.python.platform import tf_logging as _logging
from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

if "dev" in __version__:   # pylint: disable=undefined-variable
  _logging.warning("""

  TensorFlow's `tf-nightly` package will soon be updated to TensorFlow 2.0.

-   If all looks good, the reviewer will approve the PR.
-   If a change is needed, the contributor is requested to make the suggested
    change.
-   You make the change and submit it for the review again.
-   This cycle repeats itself until the PR gets approved.
-   Note: As a friendly reminder, we may reach out to you if the PR is awaiting
    your response for more than 2 weeks.

**4. Approved**

        "//tensorflow/core/platform:blocking_counter",
        "@com_google_absl//absl/strings",
        "@local_xla//xla/tsl/c:tsl_status_internal",
    ],
    alwayslink = 1,
)

exports_files(
    [
        "version_script.lds",
        "exported_symbols.lds",
    ],
    visibility = ["//visibility:public"],
)

filegroup(
    name = "checkpoint_reader_hdrs",
    srcs = [

build:linux_arm64 --copt="-mtune=generic" --copt="-march=armv8-a" --copt="-O3"


# On Windows, `__cplusplus` is wrongly defined without this switch
# See https://devblogs.microsoft.com/cppblog/msvc-now-correctly-reports-__cplusplus/
build:windows --copt=/Zc:__cplusplus
build:windows --host_copt=/Zc:__cplusplus

# Tensorflow uses M_* math constants that only get defined by MSVC headers if
# _USE_MATH_DEFINES is defined.

    *   Experimental support for quantized models with the Android GPU delegate
    *   Add GPU delegate whitelist.
    *   Rename GPU whitelist -> compatibility (list).
    *   Improve GPU compatibility list entries from crash reports.
*   NNAPI
    *   Set default value for
        `StatefulNnApiDelegate::Options::max_number_delegated_partitions` to 3.
    *   Add capability to disable `NNAPI` CPU and check `NNAPI` Errno.