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("""

  set -u
  echo '==TFCI==: Evaluated the following TFCI variables from $TFCI:'
  export -p | grep TFCI

  # Load those stored pre-existing TFCI_ vars, if any
  if [[ -s "$FROM_ENV" ]]; then
    echo '==TFCI==: NOTE: Loading the following env parameters, which were'
    echo 'already set in the shell environment. If you want to disable this'
    echo 'behavior, create a new shell.'
    cat "$FROM_ENV"
    source "$FROM_ENV"
    rm "$FROM_ENV"

    *   Added APIs for switching between interactive logging and absl logging.
        By default, Keras always writes the logs to stdout. However, this is not
        optimal in a non-interactive environment, where you don't have access to
        stdout, but can only view the logs. You can use
        `tf.keras.utils.disable_interactive_logging()` to write the logs to ABSL
        logging. You can also use `tf.keras.utils.enable_interactive_logging()`

        shape_handle, static_cast<DataType>(types[i]));
  }

  ic->set_output_handle_shapes_and_types(output.index, shape_and_type_vec);
}

// Helpers for loading a TensorFlow plugin (a .so file).
Status LoadDynamicLibrary(const char* library_filename, void** result,
                          const void** buf, size_t* len);

        "//tensorflow/lite/kernels:builtin_ops",
        "//tensorflow/lite/kernels:reference_ops",
        "//tensorflow/lite/schema:schema_fbs",
        "//tensorflow/lite/toco/logging:conversion_log_util",
        "//tensorflow/lite/toco/logging:toco_conversion_log_proto_cc",
        "//tensorflow/lite/toco:model_flags_proto_cc",
        "//tensorflow/lite/toco:toco_convert",
        "//tensorflow/lite/toco:toco_flags_proto_cc",

build:ios_x86_64 --cpu=ios_x86_64
build:ios_fat --config=ios
build:ios_fat --ios_multi_cpus=armv7,arm64,i386,x86_64

# Config to use a mostly-static build and disable modular op registration
# support (this will revert to loading TensorFlow with RTLD_GLOBAL in Python).
# By default, TensorFlow will build with a dependence on
# //tensorflow:libtensorflow_framework.so.
build:monolithic --define framework_shared_object=false

unsigned char TF_GetXlaConstantFoldingDisabled() {
  return static_cast<unsigned char>(
      tensorflow::GetBuildXlaOpsPassFlags()->tf_xla_disable_constant_folding);
}

void TF_SetXlaConstantFoldingDisabled(unsigned char should_enable) {
  tensorflow::GetBuildXlaOpsPassFlags()->tf_xla_disable_constant_folding =
      static_cast<bool>(should_enable);
}

void TF_SetXlaMinClusterSize(int size) {
  tensorflow::MarkForCompilationPassFlags* flags =