*   When you contribute a new feature to TensorFlow, the maintenance burden is
    (by default) transferred to the TensorFlow team. This means that the benefit
    of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in

        "@local_tsl//tsl/platform:ctstring",
        "@local_xla//xla/tsl/c:srcs",
    ],
    visibility = ["//visibility:public"],
)

cc_library(
    name = "pywrap_required_hdrs",
    textual_hdrs = [
        "c_api_internal.h",
        "c_api_macros.h",
        "conversion_macros.h",
        "python_api.h",
        "tensor_interface.h",
        "tf_status_helper.h",
        "tf_buffer_internal.h",

    operations such as categorical feature encoding, text vectorization, data
    normalization, and data discretization (binning). The newly added layers
    provide a replacement for the legacy feature column API, and support
    composite tensor inputs.
*   Added **categorical data** processing layers:
    *   `IntegerLookup` & `StringLookup`: build an index of categorical feature
        values

//tensorflow/python:mixed_precision
//tensorflow/python:tf_optimizer
//tensorflow/python:compare_test_proto_py
//tensorflow/python/framework:test_file_system.so
//tensorflow/python/debug:grpc_tensorflow_server.par
//tensorflow/python/feature_column:vocabulary_testdata
//tensorflow/python/util:nest_test_main_lib
//tensorflow/lite/experimental/examples/lstm:rnn_cell
//tensorflow/lite/experimental/examples/lstm:rnn_cell.py

#     dynamic_kernels:  Try to link all kernels dynamically (experimental).
#     dbg:              Build with debug info
#
# TF version options;
#     v2: Build TF v2
#
# Feature and Third party library support options:
#     xla:          Build TF with XLA
#     tpu:          Build TF with TPU support
#     cuda:         Build with CUDA support.
#     cuda_clang    Build with CUDA Clang support.