create_xml_file(links, output_path, verbose=verbose)
  if args.print:
    print_invocation_results(links)


if __name__ == '__main__':

//tensorflow/python/framework:test_ops_2
//tensorflow/python/framework:test_file_system.so
//tensorflow/python/debug:grpc_tensorflow_server.par
//tensorflow/python/feature_column:vocabulary_testdata
//tensorflow/python/util:nest_test_main_lib
//tensorflow/lite/experimental/examples/lstm:rnn_cell
//tensorflow/lite/experimental/examples/lstm:rnn_cell.py
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test

// once (i.e., on the main function side) in running distributed functions.
// This test creates a cluster with two workers, create a variable on the
// second worker, and run a distributed function (VariableAddFunction) whose ops
// span the local and remote workers. If the graph optimization pass is executed
// on both the main function side and the component function side, an error will

//tensorflow/python:compare_test_proto_py
//tensorflow/python/framework:test_file_system.so
//tensorflow/python/debug:grpc_tensorflow_server.par
//tensorflow/python/feature_column:vocabulary_testdata
//tensorflow/python/util:nest_test_main_lib
//tensorflow/lite/experimental/examples/lstm:rnn_cell
//tensorflow/lite/experimental/examples/lstm:rnn_cell.py
//tensorflow/lite/experimental/examples/lstm:unidirectional_sequence_lstm_test

  config_info_line('nogcp', 'Disable GCP support.')
  config_info_line('nonccl', 'Disable NVIDIA NCCL support.')


if __name__ == '__main__':

#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/platform/blocking_counter.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/init_main.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/net.h"
#include "tensorflow/core/platform/platform.h"
#include "tensorflow/core/platform/strcat.h"

    of other integer types) in `tf.constant`.
*   Make the `gain` argument of convolutional orthogonal initializers
    (`convolutional_delta_orthogonal`, `convolutional_orthogonal_1D`,
    `convolutional_orthogonal_2D`, `convolutional_orthogonal_3D`) have
    consistent behavior with the `tf.initializers.orthogonal` initializer, i.e.
    scale the output l2-norm by `gain` and NOT by `sqrt(gain)`. (Note that these

    TensorFlow models, please demonstrate an exploit against one of Alphabet's
    models in <https://tfhub.dev/>
  - An explanation of who can exploit this vulnerability, and what they gain
    when doing so. Write an attack scenario that demonstrates how your issue
    violates the use cases and security assumptions defined in the threat model.

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
build:windows --features=compiler_param_file
build:windows --features=archive_param_file

# Speed Windows compile times. Available in VS 16.4 (we are on 16.11). See

  result.resize(read);
  ASSERT_EQ(result, "5678") << "Result: " << result << "\n";
}

}  // namespace
}  // namespace tensorflow

GTEST_API_ int main(int argc, char** argv) {
  tensorflow::testing::InstallStacktraceHandler();
  if (!GetTmpDir()) {
    std::cerr << "Could not read GCS_TEST_TMPDIR env";
    return -1;
  }
  ::testing::InitGoogleTest(&argc, argv);