NUMPY_ANNOTATIONS = {
    "numpy": package_annotation(
        additive_build_content = """\
filegroup(
    name = "includes",
    srcs = glob(["site-packages/numpy/core/include/**/*.h"]),
)
cc_library(
    name = "numpy_headers",
    hdrs = [":includes"],
    strip_include_prefix="site-packages/numpy/core/include/",
)
""",
    ),
}

pip_parse(
    name = "pypi",
    annotations = NUMPY_ANNOTATIONS,

        "//tensorflow/lite/toco:model_flags_proto_cc",
        "//tensorflow/lite/toco:toco_convert",
        "//tensorflow/lite/toco:toco_flags_proto_cc",
        "//tensorflow/lite/toco:toco_graphviz_dump_options",
        "//tensorflow/lite/toco:toco_port",
        "//tensorflow/lite/toco:toco_tooling",
        "//tensorflow/lite/toco:tooling_util",

        construct the layer with LSTM(recurrent_activation='hard_sigmoid') to
        fallback to 1.x behavior.
*   TensorFlow Lite
    *   Move from `tensorflow/contrib/lite` to `tensorflow/lite`.
    *   Add experimental Java API for injecting TensorFlow Lite delegates
    *   Add support for strings in TensorFlow Lite Java API.
*   `tf.contrib`:
    *   Add Apache Ignite Filesystem plugin to support accessing Apache IGFS.

// which subclass it actually is. The user is responsible to use the right
// type of AbstractTensor in their context (do not pass an MlirTensor to a
// GraphContext and vice-versa).
class TracingTensorHandle : public AbstractTensorHandle {
 protected:
  explicit TracingTensorHandle(AbstractTensorHandleKind kind)
      : AbstractTensorHandle(kind) {}

 public:
  // For LLVM style RTTI.

    ],
    hdrs = ["c_api.h"],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = select({
        "//tensorflow:android": [
            "//tensorflow/core:portable_tensorflow_lib_lite",
        ],
        "//conditions:default": [
            ":immediate_execution_context",
            ":immediate_execution_distributed_manager",
            ":immediate_execution_operation",

APPLE_BAZEL_FILES = [
    'tensorflow/lite/ios/BUILD', 'tensorflow/lite/objc/BUILD',
    'tensorflow/lite/swift/BUILD',
    'tensorflow/lite/tools/benchmark/experimental/ios/BUILD'
]

# List of files to move when building for iOS.
IOS_FILES = [
    'tensorflow/lite/objc/TensorFlowLiteObjC.podspec',
    'tensorflow/lite/swift/TensorFlowLiteSwift.podspec',
]


class UserInputError(Exception):

//tensorflow/python/util:nest_test_main_lib
//tensorflow/lite/experimental/examples/lstm:rnn_cell
//tensorflow/lite/experimental/examples/lstm:rnn_cell.py
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test.py
//tensorflow/lite/python:interpreter
//tensorflow/lite/python:interpreter_test
//tensorflow/lite/python:interpreter.py

build:dbg --per_file_copt=+.*,-tensorflow.*,-xla.*@-g0
build:dbg --per_file_copt=+tensorflow/core/kernels.*@-g0
# for now, disable arm_neon. see: https://github.com/tensorflow/tensorflow/issues/33360
build:dbg --cxxopt -DTF_LITE_DISABLE_X86_NEON
# AWS SDK must be compiled in release mode. see: https://github.com/tensorflow/tensorflow/issues/37498
build:dbg --copt -DDEBUG_BUILD

# Config to build TF TPU
build:tpu --define=with_tpu_support=true

    # namespace, but they don't correspond to an actual file
    # or directory on the filesystem.
    self.packages_for_skip = [
        "tensorflow.distribute.cluster_resolver",
        "tensorflow.lite.experimental.authoring",
        "tensorflow.distribute.experimental.coordinator",
        "tensorflow.summary.experimental",
        "tensorflow.distribute.coordinator",
        "tensorflow.distribute.experimental.partitioners",

cff-version: 1.2.0
message: "If you use TensorFlow in your research, please cite it using these metadata. Software is available from tensorflow.org."
title: TensorFlow, Large-scale machine learning on heterogeneous systems