#   and scripts on Linux create a persistent container called "tf"
#   which mounts your TensorFlow directory into the container.
#
#   Important: because the container is persistent, you cannot change TFCI
#   variables in between script executions. To forcibly remove the
#   container and start fresh, run "docker rm -f tf". Removing the container
#   destroys some temporary bazel data and causes longer builds.
#

# ==============================================================================
cat <<EOF
IMPORTANT: These tests ran under docker. This script does not clean up the
container for you! You can delete the container with:

$ docker rm -f tf

You can also execute more commands within the container with e.g.:

$ docker exec tf bazel clean
$ docker exec -it tf bash
EOF

// for ResourceMgr to clean up the resource. `status` will be set.
TF_CAPI_EXPORT extern void TF_CreatePluginResource(
    TF_OpKernelContext* ctx, const char* container_name,
    const char* plugin_resource_name, void* plugin_resource,
    void (*delete_func)(void*), TF_Status* status);

// If the ResourceMgr provided by the `ctx` has a resource

limitations under the License.
==============================================================================*/
#include "tensorflow/c/eager/unified_api_testutil.h"

#include "absl/container/flat_hash_set.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/c_api_test_util.h"
#include "tensorflow/c/eager/c_api_unified_experimental.h"

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

source ~/.bashrc
VERSION=$1
REQUIREMENTS=$2

add-apt-repository ppa:deadsnakes/ppa
# Install Python packages for this container's version
cat >pythons.txt <<EOF
$VERSION
$VERSION-dev
$VERSION-venv
$VERSION-distutils
EOF
/setup.packages.sh pythons.txt

# Re-link pyconfig.h from aarch64-linux-gnu into the devtoolset directory

                          ImmediateExecutionTensorHandle* input) = 0;

  virtual ImmediateExecutionContext* GetContext() const = 0;

  // Following two methods are used to support custom device.
  // Return true if the inputs contain custom device tensor handle. It means
  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

    docker push "$TFCI_DOCKER_IMAGE"
  fi
fi

# Keep the existing "tf" container if it's already present.
# The container is not cleaned up automatically! Remove it with:
# docker rm tf
if ! docker container inspect tf >/dev/null 2>&1 ; then
  # Pass all existing TFCI_ variables into the Docker container
  env_file=$(mktemp)
  env | grep ^TFCI_ > "$env_file"

      -
        name: Set up Docker Buildx
        uses: docker/setup-buildx-action@8c0edbc76e98fa90f69d9a2c020dcb50019dc325 # v2.2.1
      -
        name: Login to GCR
        if: contains(github.event.pull_request.labels.*.name, 'build and push to gcr.io for staging')
        uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a # v2.1.0
        with:
          registry: gcr.io

          if [[ $running_containers == "" ]]; then
            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
          docker image prune -af
      - name: Clean repository
        shell: bash

    development to avoid installing the packages directly on your system (in
    which case remember to change the directory from `/root` to `/tensorflow`
    once you get into the running container so `bazel` can find the `tensorflow`
    workspace).

    you can do this by using the following command. As an example-

    ```bash
    docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10