Tensor input(tensorflow::DT_STRING, TensorShape({4}));
  for (int64_t i = 0; i < input.NumElements(); ++i) {
    tensorflow::Example example;
    auto* feature_map = example.mutable_features()->mutable_feature();
    (*feature_map)["x"].mutable_float_list()->add_value(i);
    input.flat<tstring>()(i) = example.SerializeAsString();
  }

  const tensorflow::string input_op_name(

*   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

                                                     TF_Status* status);

// Get a comma-separated list of op names executed in graph functions dispatched
// to `ctx`. This feature is currently only enabled for TFRT debug builds, for
// performance and simplicity reasons.
TF_CAPI_EXPORT extern void TFE_GetExecutedOpNames(TFE_Context* ctx,
                                                  TF_Buffer* buf,

  status->status = tensorflow::errors::Unimplemented(
      "Creating while loops is not supported on mobile. File a bug at "
      "https://github.com/tensorflow/tensorflow/issues if this feature is "
      "important to you");
  return EmptyWhileParams();
#else
  if (ninputs == 0) {
    status->status =
        InvalidArgument("TF_NewWhile() must be passed at least one input");

    *   `CategoryCrossing`: create new categorical features representing
        co-occurrences of previous categorical feature values
    *   `Hashing`: the hashing trick, for large-vocabulary categorical features
    *   `Discretization`: turn continuous numerical features into categorical
        features by binning their values
*   Improved **image preprocessing** layers: `CenterCrop`, `Rescaling`

        ":linux_aarch64",
        ":linux_armhf",
    ],
)

config_setting(
    name = "freebsd",
    values = {"cpu": "freebsd"},
    visibility = ["//visibility:public"],
)

# Features that are default ON are handled differently below.
#
config_setting(
    name = "no_gcp_support",
    define_values = {"no_gcp_support": "true"},
    visibility = ["//visibility:public"],
)

)

cc_library(
    name = "dlpack",
    srcs = ["dlpack.cc"],
    hdrs = ["dlpack.h"],
    copts = tf_copts() + [
        "-fexceptions",
        "-fno-strict-aliasing",
    ],
    features = ["-use_header_modules"],
    visibility = ["//tensorflow:__subpackages__"],
    deps = [
        ":c_api",
        ":c_api_experimental",
        ":tfe_tensorhandle_internal",

build:windows --host_copt=/D_USE_MATH_DEFINES

# Windows has a relatively short command line limit, which TF has begun to hit.
# See https://docs.bazel.build/versions/main/windows.html
build:windows --features=compiler_param_file
build:windows --features=archive_param_file

# Speed Windows compile times. Available in VS 16.4 (we are on 16.11). See
# https://groups.google.com/a/tensorflow.org/d/topic/build/SsW98Eo7l3o/discussion

    query_item: string for feature related to the variable, e.g. "CUDA for
      Nvidia GPUs".
    enabled_by_default: boolean for default behavior.
    question: optional string for how to ask for user input.
    yes_reply: optional string for reply when feature is enabled.
    no_reply: optional string for reply when feature is disabled.

  Returns:
    boolean value of the variable.

//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