{
    "description": "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.",
    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }

  size_t scheme_end = fname.find("://") + 2;
  if (fname.substr(0, scheme_end + 1) != "gs://") {
    TF_SetStatus(status, TF_INVALID_ARGUMENT,
                 "GCS path doesn't start with 'gs://'.");
    return;
  }

  size_t bucket_end = fname.find('/', scheme_end + 1);
  if (bucket_end == std::string::npos) {
    TF_SetStatus(status, TF_INVALID_ARGUMENT,

# load("//devtools/copybara/rules:copybara.bzl", "copybara_config_test")
# load("@rules_license//rules:license.bzl", "license")
# # buildifier: enable=out-of-order-load
# copybara:uncomment_end

# copybara:comment_begin(oss-only)
load("@bazel_skylib//rules:copy_file.bzl", "copy_file")
load(
    "//tensorflow/python/tools/api/generator:api_gen.bzl",
    "get_compat_files",
    "get_nested_compat_files",
)

internal communication only. It is not built for use in untrusted environments
or networks.

For performance reasons, the default TensorFlow server does not include any
authorization protocol and sends messages unencrypted. It accepts connections
from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access

        which could be challenging to read through, while not being actionable
        for end users. As of TF 2.7, TensorFlow filters internal frames in most
        errors that it raises, to keep stack traces short, readable, and focused
        on what's actionable for end users (their own code).

    This behavior can be disabled by calling

    }

    // Verify edges
    for (const EdgeSpec& e : e_edges) {
      ASSERT_TRUE(a_edges.find(e) != a_edges.end())
          << "Failed to find expected edge " << e.ToString()
          << " in fdef: " << fdef.DebugString();
    }
    for (const EdgeSpec& e : c_edges) {
      ASSERT_TRUE(a_edges.find(e) != a_edges.end())
          << "Failed to find expected control edge " << e.ToString()
          << " in fdef: " << fdef.DebugString();

      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"

  // Remove the device that has the host device name since host device is alreay
  // in an initialized context.
  for (auto d = devices.begin(); d != devices.end();) {
    if (absl::StrContains(d->get()->name(), "CPU:0")) {
      d = devices.erase(d);
    } else {
      ++d;
    }
  }

  status->status = tensorflow::unwrap(ctx)->AddDevices(std::move(devices));
}

  for (int i = 0, end = target_tensor_ids.size(); i < end; ++i) {
    const int64_t id = target_tensor_ids[i];
    if (output_gradients.empty() || output_gradients[i] == nullptr) {
      auto tensor_it = tensor_tape.find(id);
      if (tensor_it != tensor_tape.end() && tensor_it->second != -1) {
        auto op_it = op_tape.find(tensor_it->second);
        if (op_it == op_tape.end()) {
          return errors::Internal(

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
source ci/official/envs/linux_x86
TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_cpu_linux --config=tpu"
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_tpu