Build/Install - Performance - Support - Feature Request - Documentation Feature
Request - Documentation Bug - Others validations: required: true - type:
markdown attributes: value: | Please make sure that this is a bug. As per our
[GitHub Policy](https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md),we
only address code/doc bugs, performance issues, feature requests and
build/installation issues on GitHub.

  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(

namespace tensorflow {
namespace {

using ::tensorflow::error::Code;

// As we need to test multiple URI schemes we need a parameterized test.
// Furthermore, since each test creates and deletes files, we will use the same
// fixture to create new directories in `SetUp`. Each directory will reside in
// `::testing::TempDir()`, will use a RNG component and the test name. This
// ensures that two consecutive runs are unlikely to clash.

//tensorflow/python:compare_test_proto_py
//tensorflow/python/framework:test_ops_2
//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

  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");

*   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,

        "@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

        buffer() {}
} GCSFile;

void Cleanup(TF_RandomAccessFile* file) {
  auto gcs_file = static_cast<GCSFile*>(file->plugin_file);
  delete gcs_file;
}

// `google-cloud-cpp` is working on a feature that we may want to use.
// See https://github.com/googleapis/google-cloud-cpp/issues/4013.
int64_t Read(const TF_RandomAccessFile* file, uint64_t offset, size_t n,
             char* buffer, TF_Status* status) {