VerifyFDefOutputs(fdef, M({{"test_loop_exit"}}));
  VerifyFDefEdges(fdef,
                  {{"feed1", "test_loop/Enter:0"},
                   {"test_loop/Enter:output:0", "test_loop/Merge:0"},
                   {"test_loop/Merge:output:0", "test_loop/Switch:0"},
                   {"test_loop/Switch:output_false:0", "test_loop/Exit:0"},
                   {"test_loop/Exit:output:0", "test_loop_exit"}},
                  {}, false);
}

    if (new_node == nullptr) continue;
    parent->name_map[new_node->name()] = new_node;
  }

  // Populate 'outputs'.
  DCHECK_LE(loop_outputs.size(), num_loop_vars);
  for (int i = 0; i < loop_outputs.size(); ++i) {
    outputs[i] = {ToOperation(loop_outputs[i].node()), loop_outputs[i].index()};
  }
}

}  // namespace
#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)

  """
  var = environ_cp.get(var_name)
  if not var:
    var = get_input(ask_for_var)
    print('\n')
  if not var:
    var = var_default
  return var


def prompt_loop_or_load_from_env(environ_cp,
                                 var_name,
                                 var_default,
                                 ask_for_var,
                                 check_success,

        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
    ],
)

tf_cc_test(
    name = "while_loop_test",
    size = "medium",
    srcs = ["while_loop_test.cc"],
    deps = [
        ":c_api",
        ":c_test_util",
        "//tensorflow/core:lib",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",

  TF_Output cond_output;

  // The loop body graph. The inputs are the current values of the loop
  // variables. The outputs are the updated values of the loop variables.
  TF_Graph* const body_graph;
  const TF_Output* const body_inputs;
  TF_Output* const body_outputs;

  // Unique null-terminated name for this while loop. This is used as a prefix
  // for created operations.
  const char* name;
} TF_WhileParams;

  CONFIG_TOGGLE(auto_mixed_precision_onednn_bfloat16);
  CONFIG_TOGGLE(auto_mixed_precision_mkl);
  CONFIG_TOGGLE(pin_to_host_optimization);
  CONFIG_TOGGLE(layout_optimizer);
  CONFIG_TOGGLE(remapping);
  CONFIG_TOGGLE(loop_optimization);
  CONFIG_TOGGLE(dependency_optimization);
  CONFIG_TOGGLE(auto_parallel);
  CONFIG_TOGGLE(memory_optimization);
  CONFIG_TOGGLE(scoped_allocator_optimization);

  // Returns a map from variable names to their data types.  Slices of a
  // partitioned tensor are combined into a single entry.
  const TensorSliceReader::VarToDataTypeMap& GetVariableToDataTypeMap() const;

  // Attempts to look up the tensor named "name" and stores the found result in
  // "out_tensor".
  void GetTensor(const string& name,
                 std::unique_ptr<tensorflow::Tensor>* out_tensor,
                 TF_Status* out_status) const;

    bazel test $TFCI_BAZEL_COMMON_ARGS //tensorflow/tools/api/tests:api_compatibility_test
    echo "You have to re-generate the TF API goldens and have the API changes reviewed."
    echo "Look at the instructions for ':api_compatibility_test -- --update_goldens=True'"
}

teardown_file() {
    bazel shutdown

  // Compare with first GraphDef + added NodeDef.
  NodeDef* added_node = graph_def.add_node();
  *added_node = node_def;
  EXPECT_EQ(graph_def.DebugString(), graph_def2.DebugString());

  // Look up some nodes by name.
  TF_Operation* neg2 = TF_GraphOperationByName(graph, "neg");
  EXPECT_TRUE(neg == neg2);
  NodeDef node_def2;
  ASSERT_TRUE(GetNodeDef(neg2, &node_def2));

## Known Caveats

*   TF Core:
    *   A longstanding bug in `tf.while_loop`, which caused it to execute
        sequentially, even when `parallel_iterations>1`, has now been fixed.
        However, the increased parallelism may result in increased memory use.
        Users who experience unwanted regressions should reset their
        `while_loop`'s `parallel_iterations` value to 1, which is consistent
        with prior behavior.