bool found_neg = false;
  size_t pos = 0;
  TF_Operation* oper;
  while ((oper = TF_GraphNextOperation(graph, &pos)) != nullptr) {
    if (oper == feed) {
      EXPECT_FALSE(found_placeholder);
      found_placeholder = true;
    } else if (oper == three) {
      EXPECT_FALSE(found_scalar_const);
      found_scalar_const = true;
    } else if (oper == add) {
      EXPECT_FALSE(found_add);
      found_add = true;

the local wheel file specified in the `requirements_lock_<python_version>.txt`.

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.

  if (num_dims > TensorShape::MaxDimensions()) {
    return errors::InvalidArgument("Value specified for `", attr_name, "` has ",
                                   num_dims,
                                   " dimensions which is over the limit of ",
                                   TensorShape::MaxDimensions(), ".");
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {

  // case placement information is lost when resolving the handle.
  //
  // If false, a caller might implement pretty-printing by resolving and
  // iterating over the resulting tensor. This may still be viable if resolving
  // the handle loses information, but `SummarizeValue` would be more precise.
  virtual bool PreferCustomSummarizer() const { return false; }

    } else {
      // Remove the stale block and continue.
      RemoveFile_Locked(key.first);
    }
  }

  // Insert a new empty block, setting the bookkeeping to sentinel values
  // in order to update them as appropriate.
  auto new_entry = std::make_shared<Block>();
  lru_list_.push_front(key);
  lra_list_.push_front(key);
  new_entry->lru_iterator = lru_list_.begin();

//
// As some filesystems require special setup, these tests are run manually.
//
// Each filesystem implementation can be provided by DSOs, so we provide the
// `--dsos` flag to specify a list of shared objects to be loaded in order.
// If the flag is not used, no shared objects are loaded.
//
// Every filesystem provides support for accessing URIs of form
// `[<scheme>://]<path>` where `<scheme>` is optional (if missing, we are

              "localhost:", tensorflow::testing::PickUnusedPortOrDie())});

  tensorflow::JobDef* job_def2 = cluster_def->add_job();
  job_def2->set_name("worker");

  // Copy over `host:port` at `task_index`
  for (auto task : cluster_server_def.cluster().job(0).tasks()) {
    if (task.first == task_index) {
      job_def2->mutable_tasks()->insert({task.first, task.second});
    }
  }

# limitations under the License.
# ==============================================================================

# Bring in all of the public TensorFlow interface into this
# module.

# pylint: disable=g-bad-import-order
from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import

from tensorflow.python.platform import flags  # pylint: disable=g-import-not-at-top

  TF_DeleteBuffer(buffer);
  TF_DeleteStatus(s);
  return ret;
}

bool GetNodeDef(TF_Operation* oper, tensorflow::NodeDef* node_def) {
  TF_Status* s = TF_NewStatus();
  TF_Buffer* buffer = TF_NewBuffer();
  TF_OperationToNodeDef(oper, buffer, s);
  bool ret = TF_GetCode(s) == TF_OK;
  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

const char* TF_OperationName(TF_Operation* oper) {
  return oper->node.name().c_str();
}

const char* TF_OperationOpType(TF_Operation* oper) {
  return oper->node.type_string().c_str();
}

const char* TF_OperationDevice(TF_Operation* oper) {
  return oper->node.requested_device().c_str();
}

int TF_OperationNumOutputs(TF_Operation* oper) {
  return oper->node.num_outputs();
}