Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst);

namespace {

static void ExpectHasSubstr(StringPiece s, StringPiece expected) {
  EXPECT_TRUE(absl::StrContains(s, expected))
      << "'" << s << "' does not contain '" << expected << "'";
}

// Returns the GPU device name if there is one (with arbitrary tie breaking if
// there are more than one), or "" otherwise.
string GPUDeviceName(TF_Session* session) {

    } else {
      status->status = AddSymbolicGradients(scope, y_arg, x_arg, &dy_arg);
    }

    // Update g->name_map with the name_map from the scope, which will contain
    // the new gradient ops.
    for (int i = first_new_node_id; i < g->graph.num_node_ids(); ++i) {
      Node* n = g->graph.FindNodeId(i);
      if (n == nullptr) continue;

// which is passed to the functions referenced in the TFE_CustomDevice struct
// `device` (execute, delete_device, etc.). It can for example contain the
// names of wrapped devices.
//
// There are currently no graph semantics implemented for registered custom
// devices, so executing tf.functions which contain operations placed on the
// custom devices will fail.
//
// `device_name` must not name an existing physical or custom device. It must

*   Tensorflow code now produces 2 different pip packages: `tensorflow_core`
    containing all the code (in the future it will contain only the private
    implementation) and `tensorflow` which is a virtual pip package doing
    forwarding to `tensorflow_core` (and in the future will contain only the
    public API of tensorflow). We don't expect this to be breaking, unless you
    were importing directly from the implementation.

# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then
# gathers the list of all packages (i.e. directories) which contain those
# targets.
license_query() {
 bazel cquery --experimental_cc_shared_library "$1" --keep_going \
  | grep -e "^//" -e "^@" \
  | grep -E -v "^//tensorflow" \
  | sed -e 's|:.*||' \
  | sort -u
}

    ask_for_var: (String) string for how to ask for user input.
    check_success: (Function) function that takes one argument and returns a
      boolean. Should return True if the value provided is considered valid. May
      contain a complex error message if error_msg does not provide enough
      information. In that case, set suppress_default_error to True.
    error_msg: (String) String with one and only one '%s'. Formatted with each

             : absl::OkStatus();
}

// Converts `ninputs` and `inputs` into `inputs_tensors` and `input_nodes` and
// does various checks while doing so. `input_nodes` will contain the same
// information as input_tensors just in a different structure to make
// following processing easier. TODO(iga): Simplify this nested structure.
Status ProcessInputs(