# match exactly sha256:<hash> because curl may include a ^M symbol at
          # the end of the line.
          # See https://cloud.google.com/architecture/using-container-images#exploring_image_manifests_digests_and_tags
          echo -n "Trying to map name $1 to tag $2... "
          digest=$(curl -s --head "https://gcr.io/v2/tensorflow-sigs/build/manifests/$2" | egrep -o "sha256:[[:alnum:]]*")

        "//tensorflow/core/platform:status",
        "@com_google_absl//absl/log",
    ],
    alwayslink = 1,
)

# Library implementing helper functionality, so that the above only contains
# the API implementation for modular filesystems.
cc_library(
    name = "posix_filesystem_helper",
    srcs = ["posix_filesystem_helper.cc"],
    hdrs = ["posix_filesystem_helper.h"],
    deps = [":copy_file"],
)

        with:
          registry: gcr.io
          username: _json_key
          password: ${{ secrets.GCP_CREDS }}
      -
        name: Grab the upcoming TF version to tag this container
        run: |
          # [[:digit:]] searches for numbers and \+ joins them together
          major_version=$(grep "^#define TF_MAJOR_VERSION" ./tensorflow/core/public/version.h | grep -o "[[:digit:]]\+")

#   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

# 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;

// type of eager and graph tensors. It is also the result of executing an
// operation.
typedef struct TF_AbstractTensor TF_AbstractTensor;

// A TF_AbstractOp is the metadata we need to execute an operation. E.g. this
// could contain the op type and other attributes.
typedef struct TF_AbstractOp TF_AbstractOp;

// Stores a function representation that can be used for execution or for
// setting functional attributes of other composite ops e.g. control flow.

// 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"