# Usage: setup.python.sh <pyversion> <requirements.txt>

# Sets up custom apt sources for our TF images.

# Prevent apt install tzinfo from asking our location (assumes UTC)
export DEBIAN_FRONTEND=noninteractive

# Set up shared custom sources
apt-get update
apt-get install -y gnupg ca-certificates

# Deadsnakes: https://launchpad.net/~deadsnakes/+archive/ubuntu/ppa

    bazel by doing as follows:

    ```bash
    export flags="--config=opt -k"
    ```

    If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and
    add the `cuda` option flag

    ```bash
    export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH"
    export flags="--config=opt --config=cuda -k"
    ```

    DCHECK_EQ(nullptr, session);
    return nullptr;
  }
}

TF_Session* TF_LoadSessionFromSavedModel(
    const TF_SessionOptions* session_options, const TF_Buffer* run_options,
    const char* export_dir, const char* const* tags, int tags_len,
    TF_Graph* graph, TF_Buffer* meta_graph_def, TF_Status* status) {
// TODO(sjr): Remove the IS_MOBILE_PLATFORM guard. This will require ensuring

}

TF_CAPI_EXPORT extern int TFE_OpGetInputLength(TFE_Op* op,
                                               const char* input_name,
                                               TF_Status* status) {
  int ret = -1;
  status->status = tensorflow::unwrap(op)->InputLength(input_name, &ret);
  return ret;
}

TF_CAPI_EXPORT extern int TFE_OpGetOutputLength(TFE_Op* op,

# with ample storage. When this variable is empty (i.e by default), Bazel will
# use the output base directory to run tests.
if [[ "${TFCI_MACOS_BAZEL_TEST_DIR_ENABLE}" == 1 ]]; then
  mkdir -p "${TFCI_MACOS_BAZEL_TEST_DIR_PATH}"
  export TEST_TMPDIR="${TFCI_MACOS_BAZEL_TEST_DIR_PATH}"
fi

# "TFCI_MACOS_INSTALL_BAZELISK_ENABLE" is used to decide if we need to install
# Bazelisk manually. We enable this for macOS x86 builds as those VMs do not

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  tfrun python3 tensorflow/tools/ci_build/update_version.py --nightly
  # replace tensorflow to tf_nightly in the wheel name
  export TFCI_BUILD_PIP_PACKAGE_ARGS="$(echo $TFCI_BUILD_PIP_PACKAGE_ARGS | sed 's/tensorflow/tf_nightly/')"
fi

tfrun bazel build $TFCI_BAZEL_COMMON_ARGS //tensorflow/tools/pip_package:wheel $TFCI_BUILD_PIP_PACKAGE_ARGS

performance limitations.

## Multi-Tenant environments

It is possible to run multiple TensorFlow models in parallel. For example,
`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the

    visibility = ["//visibility:public"],
)

# DO NOT ADD ANY NEW EXCEPTIONS TO THIS LIST!
# Instead, please use public APIs or public build rules TF provides.
# If you need functionality that is not exposed, we will work with you to expand our public APIs.
# TODO(b/173549186): Move Google-internal TF code out of learning/brain
package_group(
    name = "internal",
    packages = [

  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return -1;
  }
  return tensorflow::unwrap(h)->DeviceId(&status->status);
}

TF_CAPI_EXPORT extern void TFE_TensorHandleGetStatus(TFE_TensorHandle* h,
                                                     TF_Status* status) {
  status->status = tensorflow::unwrap(h)->TensorHandleStatus();
}

TF_CAPI_EXPORT extern int TF_OperationNumOutputs(TF_Operation* oper);
TF_CAPI_EXPORT extern TF_DataType TF_OperationOutputType(TF_Output oper_out);
TF_CAPI_EXPORT extern int TF_OperationOutputListLength(TF_Operation* oper,
                                                       const char* arg_name,
                                                       TF_Status* status);