* If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](https://code.google.com/legal/individual-cla-v1.0.html).
  * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](https://code.google.com/legal/corporate-cla-v1.0.html).

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

# Need a newer version of patchelf as the installed version is buggy in 20.04
# so get patchelf source from 22.04 ie 'jammy' and build it to avoid dependency
# problems that would occur with a binary package

mkdir -p /patchelf

   */
  // Define
  TF_Operation* feed = Placeholder(func_graph_, s_);
  Define(-1, {}, {feed}, {feed}, {});

  // Use, run, and verify
  TF_Operation* func_feed = Placeholder(host_graph_, s_);
  TF_Operation* func_op = Use({func_feed});
  Run({{func_feed, Int32Tensor(3)}}, func_op, 3);
  VerifyFDef(empty_, {{"feed_0", DT_INT32}}, {{"feed", DT_INT32}},
             {{"feed_0", "feed"}}, {});
}

set -euxo pipefail

for wheel in /tf/pkg/*.whl; do
  echo "Checking and renaming $wheel..."
  time python3 -m auditwheel repair --plat manylinux2014_aarch64 "$wheel" --wheel-dir /tf/pkg 2>&1 | tee check.txt

  # We don't need the original wheel if it was renamed
  new_wheel=$(grep --extended-regexp --only-matching '/tf/pkg/\S+.whl' check.txt)
  if [[ "$new_wheel" != "$wheel" ]]; then
    rm "$wheel"
    wheel="$new_wheel"
  fi

typedef enum TF_TriState {
  TF_TriState_Default = 0,
  TF_TriState_Off,
  TF_TriState_On,
} TF_TriState;

// TF_GrapplerItem represents a combination of a graph, one of more fetch nodes,
// and potentially a set of nodes to feed.
typedef struct TF_GrapplerItem TF_GrapplerItem;

// Flags indicating whether existing optimizers should be turned off.
// It's optional for plugin to set functions to return true/false. If not

#   destroys some temporary bazel data and causes longer builds.
#
#   You will need the NVIDIA Container Toolkit for GPU testing:
#   https://github.com/NVIDIA/nvidia-container-toolkit
#
#   Note: if you interrupt a bazel command on docker (ctrl-c), you
#   will need to run `docker exec tf pkill bazel` to quit bazel.
#
#   Note: new files created from the container are owned by "root".

  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;

  virtual Status InputLength(const char* input_name, int* length) = 0;

         * It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.
         * All you need is a good internet connection and you are all set.
      * Try to build TF from sources by changing CPU optimization flags.
   
   *Please let us know if this helps.*
   
windows_comment: >

install_deps()

# Initialize the TensorFlow repository and all dependencies.
#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

5) Run the `updater.sh` shell script. \
   If the base requirements file hasn't yet been updated to account for the new
   Python version, which will require different versions for at least some
   dependencies, it will need to be updated now, for the script to run
   successfully.

6) A new `requirements_lock_3_11.txt` file should appear under the root of the