Packages are imported one by one in alphabetical order during runtime.

The test doesn't identify package's order-dependent issues; for instance,
importing "tf.foo" followed by "tf.bar" won't reveal that "tf.bar" depends on
"tf.foo" being imported first.

The `_api/v2/api_packages.txt` file is generated during the TensorFlow API v2
init files creation process and is subsequently stored in the wheel file after

                                             "description", "label1", "label2");
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);
  auto* cell2 = TFE_MonitoringGetCellCounter2(counter2, "foo", "bar");
  TFE_MonitoringCounterCellIncrementBy(cell2, 2);
  EXPECT_EQ(TFE_MonitoringCounterCellValue(cell2), 2);

  TFE_MonitoringDeleteCounter1(counter1);
  TFE_MonitoringDeleteCounter2(counter2);
}

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

  // Attempt to create node in g2 with input from g1
  Neg(feed, g2, s);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s));
  EXPECT_STREQ("foo", TF_Message(s));

  TF_DeleteGraph(g1);
  TF_DeleteGraph(g2);
  TF_DeleteStatus(s);
}
*/

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

from tensorflow.python import tf2 as _tf2
_tf2.enable()

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

# Make sure directory containing top level submodules is in
# the __path__ so that "from tensorflow.foo import bar" works.
# We're using bitwise, but there's nothing special about that.
_API_MODULE = _sys.modules[__name__].bitwise
_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))

    * `pip install -U "tensorflow==1.*"`

  Otherwise your code may be broken by the change.

  """)

# Make sure directory containing top level submodules is in
# the __path__ so that "from tensorflow.foo import bar" works.
# We're using bitwise, but there's nothing special about that.
_API_MODULE = _sys.modules[__name__].bitwise  # pylint: disable=undefined-variable
_current_module = _sys.modules[__name__]

# Setup tfrun, a helper function for executing steps that can either be run
# locally or run under Docker. setup_docker.sh, below, redefines it as "docker
# exec".
# Important: "tfrun foo | bar" is "( tfrun foo ) | bar", not "tfrun (foo | bar)".
# Therefore, "tfrun" commands cannot include pipes -- which is
# probably for the better. If a pipe is necessary for something, it is probably

fi
source ci/official/utilities/get_versions.sh

# Note on gsutil commands:
# "gsutil cp" always "copies into". It cannot act on the contents of a directory
# and it does not seem possible to e.g. copy "gs://foo/bar" as anything other than
# "/path/bar". This script uses "gsutil rsync" instead, which acts on directory
# contents. About arguments to gsutil:
# "gsutil -m rsync" runs in parallel.

  DefineFunction(func_name_, &func_);

  // Get non existent attribute
  TF_Buffer* attr_buf = TF_NewBuffer();
  TF_FunctionGetAttrValueProto(func_, "foo_attr", attr_buf, s_);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_));
  EXPECT_EQ(string("Function 'MyFunc' has no attr named 'foo_attr'."),
            string(TF_Message(s_)));
  TF_DeleteBuffer(attr_buf);

  // Set attr
  tensorflow::AttrValue attr;

*   Improved support for NumPy style basic slicing including non-1 strides,
    ellipses, newaxis, and negative indices. For example complicated expressions
    like `foo[1, 2:4, tf.newaxis, ..., :-3:-1, :]` are now supported. In
    addition we have preliminary (non-broadcasting) support for sliced
    assignment to variables. In particular one can write
    `var[1:3].assign([1,11,111])`.