":grappler",
        "//tensorflow/c:c_api_internal",
        "//tensorflow/c:tf_buffer_internal",
        "//tensorflow/core:framework",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/grappler:grappler_item",
        "//tensorflow/core/grappler/clusters:single_machine",
        "//tensorflow/core/grappler/costs:graph_properties",

          f" {len(failed_packages)}/{len(self.api_packages_v2)} packages"
          f" {version}:\n{failed_packages}"
      )
    logging.info("Import of packages was successful.")


if __name__ == "__main__":

    deps = [
        ":ram_file_block_cache",
        "//tensorflow/c:tf_status_internal",
        "//tensorflow/core:lib",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/platform:blocking_counter",
        "//tensorflow/core/platform/cloud:now_seconds_env",
    ],
)

tf_cc_test(
    name = "gcs_filesystem_test",
    srcs = [

build --copt=-Wno-gnu-offsetof-extensions

# Store performance profiling log in the mounted artifact directory.
# The profile can be viewed by visiting chrome://tracing in a Chrome browser.
# See https://docs.bazel.build/versions/main/skylark/performance.html#performance-profiling
build --profile=/tf/pkg/profile.json.gz

# Use the rebuilt gcc toolchain to compile for manylinux2014
build --crosstool_top="@ml2014_clang_aarch64_config_aarch64//crosstool:toolchain"

# the `VERSION` parameter
  # Download binary libstdc++ 4.8 shared library release
wget --retry-connrefused --waitretry=1 --read-timeout=20 --timeout=15 --tries=5 "http://old-releases.ubuntu.com/ubuntu/pool/main/g/gcc-4.8/libstdc++6_4.8.1-10ubuntu8_arm64.deb" && \
    unar "libstdc++6_4.8.1-10ubuntu8_arm64.deb" && \
    tar -C "${TARGET}" -xvzf "libstdc++6_4.8.1-10ubuntu8_arm64/data.tar.gz" "./usr/lib/aarch64-linux-gnu/libstdc++.so.6.0.18"  && \

        "//tensorflow/core/tfrt/common:async_value_tensor",
        "//tensorflow/core/tfrt/common:pjrt_util",
        "@com_google_absl//absl/log:check",
        "@com_google_googletest//:gtest_main",
        "@local_tsl//tsl/lib/core:status_test_util",
        "@local_tsl//tsl/platform:casts",
        "@local_tsl//tsl/platform:status_matchers",
        "@local_tsl//tsl/protobuf:error_codes_proto_impl_cc",

The Code of Conduct also applies within project spaces and in public spaces whenever an individual is representing TensorFlow or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed or de facto representative at an online or offline event. 


## Conflict Resolution

    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.