TF_DeviceList* devices = TFE_ContextListDevices(ctx, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_DeleteContext(ctx);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  const int num_devices = TF_DeviceListCount(devices);
  EXPECT_GE(num_devices, 1) << "At least one CPU device should exist";
  for (int i = 0; i < num_devices; ++i) {

  float* m_float = static_cast<float*>(TF_TensorData(m_data));
  TF_DeviceList* devices = TFE_ContextListDevices(ctx, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  int num_devices = TF_DeviceListCount(devices);
  for (int d = 0; d < num_devices; ++d) {
    const char* name = TF_DeviceListName(devices, d, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TF_DeviceList* device_list = list.get();

  CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  const int num_devices = TF_DeviceListCount(device_list);
  LOG(INFO) << "There are " << num_devices << " devices.";
  for (int i = 0; i < num_devices; ++i) {
    const char* device_name = TF_DeviceListName(device_list, i, s);
    CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

+    __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) \
-  extern __typeof (name) aliasname __attribute__ ((weak, alias (#name)));

// `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
// follow the format:
//

    seeds when they are not given explicitly (most often).
*   The following APIs are not longer experimental:
    `tf.config.list_logical_devices`, `tf.config.list_physical_devices`,
    `tf.config.get_visible_devices`, `tf.config.set_visible_devices`,
    `tf.config.get_logical_device_configuration`,
    `tf.config.set_logical_device_configuration`.

build:windows --copt=/Zc:__cplusplus
build:windows --host_copt=/Zc:__cplusplus

# Tensorflow uses M_* math constants that only get defined by MSVC headers if
# _USE_MATH_DEFINES is defined.
build:windows --copt=/D_USE_MATH_DEFINES
build:windows --host_copt=/D_USE_MATH_DEFINES

# Windows has a relatively short command line limit, which TF has begun to hit.
# See https://docs.bazel.build/versions/main/windows.html

  // Not owned; provides operations on Tensors which are currently only
  // available in language bindings (e.g. Python).
  const VSpace<Gradient, BackwardFunction, TapeTensor>& vspace_;

  // Decides if tangents are vectorized or not
  bool use_batch_;

  struct AccumulatorCallState {
    AccumulatorCallState(
        GradientTape<Gradient, BackwardFunction, TapeTensor>* backward_tape,
        bool accumulating)

-#  endif
-# endif
-
-/* GCC < 3.0 optimizes memset(s, 0, n) but not bzero(s, n).
-   The optimization is broken before EGCS 1.1.
-   GCC 3.0+ has __builtin_bzero as well, but at least till GCC 3.4
-   if it decides to call the library function, it calls memset
-   and not bzero.  */
-# if __GNUC_PREREQ (2, 91)
-#  define __bzero(s, n) __builtin_memset (s, '\0', n)
-# endif
-

      print('Found possible Python library paths:\n  %s' %
            '\n  '.join(python_lib_paths))
      default_python_lib_path = python_lib_paths[0]
      python_lib_path = get_input(
          'Please input the desired Python library path to use.  '
          'Default is [{}]\n'.format(python_lib_paths[0]))
      if not python_lib_path:
        python_lib_path = default_python_lib_path
    environ_cp['PYTHON_LIB_PATH'] = python_lib_path