EXPECT_EQ(string("fake"), string(src_names[0]));
  EXPECT_EQ(0, src_indexes[0]);

  TF_DeleteImportGraphDefResults(results);
  TF_DeleteImportGraphDefOptions(opts);
  TF_DeleteBuffer(graph_def);
  TF_DeleteGraph(graph);
  TF_DeleteStatus(s);
}

TEST(CAPI, Session) {
  TF_Status* s = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Make a placeholder operation.

        `tf.quantization.dequantize`,
        `tf.quantization.fake_quant_with_min_max_args`,
        `tf.quantization.fake_quant_with_min_max_args_gradient`,
        `tf.quantization.fake_quant_with_min_max_vars`,
        `tf.quantization.fake_quant_with_min_max_vars_gradient`,
        `tf.quantization.fake_quant_with_min_max_vars_per_channel`,
        `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient`.

from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader

# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.
_os.environ["TF2_BEHAVIOR"] = "1"
from tensorflow.python import tf2 as _tf2
_tf2.enable()

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

# Make sure directory containing top level submodules is in

// directly, instead of requiring us to serialize to a GraphDef and
// call Session::Extend().
bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status) {
  if (session->graph != nullptr) {
    // Take the graph lock before the session lock to avoid deadlock. This is
    // safe since session->graph does not change.
    session->graph->mu.lock();
    mutex_lock session_lock(session->mu);

+  extern __typeof (name) aliasname __attribute__ ((alias (#name))) \
+    __attribute_copy__ (name);
 
 /* This comes between the return type and function name in
    a function definition to make that definition weak.  */
@@ -125,14 +131,16 @@
    If weak aliases are not available, this defines a strong alias.  */
 # define weak_alias(name, aliasname) _weak_alias (name, aliasname)
 # define _weak_alias(name, aliasname) \

  # non-interactive shells as well.
  shopt -s expand_aliases
else
  echo '==TFCI==: Error: Cannot find path to grealpath or gstat'
  echo 'TF CI scripts require GNU core utilties to be installed. Please make'
  echo 'sure they are present on your system and try again.'
  exit 1
fi

# "TFCI_MACOS_BAZEL_TEST_DIR_PATH" specifies the directory that Bazel should use

Eager mode lets users write imperative-style statements that can be easily
inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

# so I am not sure how to verify that the dependencies themselves are valid for
# the moment.
if [[ "$TFCI_BAZEL_COMMON_ARGS" =~ gpu|cuda ]]; then
  echo "Checking to make sure tensorflow[and-cuda] is installable..."
  "$python" -m pip install "$(echo *.whl)[and-cuda]" $TFCI_PYTHON_VERIFY_PIP_INSTALL_ARGS

      /*run_eager_op_as_function=*/opts->run_eager_op_as_function,
      /*jit_compile_rewrite=*/opts->jit_compile_rewrite);
#if !defined(IS_MOBILE_PLATFORM)
  eager_context->SetDistributedManager(
      std::make_unique<tensorflow::EagerContextDistributedManager>(
          eager_context));
#endif  // !IS_MOBILE_PLATFORM
  return tensorflow::wrap(eager_context);
}

void TFE_DeleteContext(TFE_Context* ctx) {

  file_block_cache = std::make_unique<RamFileBlockCache>(
      block_size, max_bytes, max_staleness,
      [this](const std::string& filename, size_t offset, size_t buffer_size,
             char* buffer, TF_Status* status) {
        return LoadBufferFromGCS(filename, offset, buffer_size, buffer, this,
                                 status);
      });
  stat_cache = std::make_unique<ExpiringLRUCache<GcsFileStat>>(