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

limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_C_EAGER_GRADIENTS_H_
#define TENSORFLOW_C_EAGER_GRADIENTS_H_

#include "absl/container/flat_hash_map.h"
#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/tape.h"
#include "tensorflow/core/common_runtime/eager/attr_builder.h"

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
# have Bazelisk pre-installed. "TFCI_MACOS_INSTALL_BAZELISK_URL" contains the
# link to the Bazelisk binary which needs to be downloaded.
if [[ "${TFCI_MACOS_INSTALL_BAZELISK_ENABLE}" == 1 ]]; then
  sudo wget --no-verbose -O "/usr/local/bin/bazel" "${TFCI_MACOS_INSTALL_BAZELISK_URL}"

// graph_def.library().gradient()
std::vector<std::pair<string, string>> GetGradDefs(
    const tensorflow::GraphDef& graph_def);

// Returns a sorted vector of names contained in `grad_def`
std::vector<string> GetFuncNames(const tensorflow::GraphDef& graph_def);

class CSession {
 public:
  CSession(TF_Graph* graph, TF_Status* s, bool use_XLA = false);

          # 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:]]\+")

The CI folder contains the configuration files and scripts used to build, test,
and deploy TensorFlow. This folder is typically used by continuous integration
(CI) tools to build and test TensorFlow whenever there is a change to the
code. This folder is broken into subfolders that represent the level of support

# JAX requirements, passed into container by defining the ARG 
# REQUIREMENTS_FILE=jax.requirements.txt


setuptools
wheel
cloudpickle
colorama>=0.4.4
matplotlib
pillow>=9.1.0
rich
absl-py
portpicker
six
opt-einsum
auditwheel
typing_extensions
importlib_metadata>=4.6
numpy==1.26.0;python_version=="3.12"
numpy==1.23.4;python_version=="3.11"
numpy==1.22.4;python_version<"3.11"
scipy==1.11.2;python_version=="3.12"

// registering traced functions.
// Operations creation within a context can only be executed in that context
// (for now at least).
// Implementations of the context may contain some state e.g. an execution
// environment, a traced representation etc.
class AbstractContext {
 protected:
  enum AbstractContextKind { kGraph, kMlir, kEager, kTfrt, kTape, kOpHandler };