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.

If you open a GitHub Issue, here is our policy:

1.  It must be a bug/performance issue or a feature request or a build issue or
    a documentation issue (for small doc fixes please send a PR instead).
1.  Make sure the Issue Template is filled out.
1.  The issue should be related to the repo it is created in.

**Here's why we have this policy:** We want to focus on the work that benefits
the whole community, e.g., fixing bugs and adding features. Individual support

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(

// setting functional attributes of other composite ops e.g. control flow.
typedef struct TF_AbstractFunction TF_AbstractFunction;

// This allows the client to swap the implementation of the tracing engine.
// Any future call to TF_CreateFunction will use the implementation defined
// here.
void TF_SetTracingImplementation(const char* name, TF_Status*);

// Creates a new TensorFlow function. A Function is an execution context, and as

*   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

//     * Note that the API is still under active development and doesn't have
//       versioning guarantees yet.
//   * `void* ext` is a free-form field that can be populated by
//     a plugin in `TP_*` structs or potential future extension points .
//
// Example usage:
//
//   /* Sample TensorFlow code below, exact implementation might differ. */
//   // Version checking uses `struct_size`. It should be set both by core
//   // and the plugin.

  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");

index 185402f..bbdeae0 100644
--- a/sysdeps/aarch64/dl-machine.h
+++ b/sysdeps/aarch64/dl-machine.h
@@ -49,23 +49,11 @@ elf_machine_load_address (void)
   /* To figure out the load address we use the definition that for any symbol:
      dynamic_addr(symbol) = static_addr(symbol) + load_addr
 
-     The choice of symbol is arbitrary. The static address we obtain

//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