e.g.:
    bash update_requirements.sh /tmp/tensorflow-2.14.0-cp311-cp311-linux_x86_64.whl 3_11
    ```

#### Requirements Updater Script
This script automates the process of updating TensorFlow requirements for a
specific Python version.

##### Parameters
`path_to_tensorflow_wheel`: The local path to the TensorFlow wheel file.
Example: `/tmp/tensorflow-2.14.0-cp311-cp311-linux_x86_64.whl`

# Sourcing this enables local disk cache
if [[ $(uname -s) == "Darwin" ]]; then
  echo "Please note that using disk cache on macOS is not recommended because the"
  echo "cache can end up being pretty big and make the build process inefficient."
fi

// 6. Create a context D using the full cluster server def E.
// 7. Read the variable in D.
TEST(CAPI, SingleHostServerDefV1Works) {
  // Create a server def that represents a 2-process cluster and a client.
  // Example:
  //
  // cluster { job { name: "worker"
  //                 tasks { key: 0 value: "localhost:14522" }
  //                 tasks { key: 1 value: "localhost:14523" }

#include <limits.h>
#include <memory.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <unistd.h>

#ifdef _WIN32
#include <process.h>
#endif

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_experimental.h"
#include "tensorflow/c/env.h"
#include "tensorflow/c/kernels.h"

# 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
cd /patchelf
echo deb-src http://ports.ubuntu.com/ubuntu-ports/ jammy universe>>/etc/apt/sources.list
apt-get update
apt-get -y build-dep patchelf/jammy

To specify the version via a Bazel command argument, use the following:

```
--repo_env=TF_PYTHON_VERSION=3.11
```

## Requirements updater

Requirements updater is a standalone tool, intended to simplify process of
updating requirements for multiple minor versions of Python.

It takes in a file with a set of dependencies, and produces a more detailed
requirements file for each version, with hashes specified for each

// context_id is initialized with a dummy value and is later set when the worker
// is initialized (either locally or remotely). The context_id can change during
// the process lifetime although this should cause the worker to be
// reinitialized (e.g. cleared caches) as well.
TF_CAPI_EXPORT extern uint64_t TFE_GetContextId(TFE_Context* ctx);

### `tf.data`:

*   Adds new `tf.data.experimental.service.register_dataset` and
    `tf.data.experimental.service.from_dataset_id` APIs to enable one process to
    register a dataset with the tf.data service, and another process to consume
    data from the dataset.
*   Adds support for dispatcher fault tolerance. To enable fault tolerance,
    configure a `work_dir` when running your dispatcher server and set

# For projects which use TensorFlow as part of a Bazel build process, putting
# nothing in a bazelrc will default to a monolithic build. The following line
# opts in to modular op registration support by default.
build --define framework_shared_object=true
build --define tsl_protobuf_header_only=true

build --define=use_fast_cpp_protos=true
build --define=allow_oversize_protos=true

build --spawn_strategy=standalone
build -c opt

  // Process inputs.
  std::vector<tensorflow::OutputTensor> input_tensors;
  std::unordered_map<const Node*, std::vector<int>> input_nodes;
  status->status = tensorflow::ProcessInputs(fn_body, fn_name, ninputs, inputs,
                                             &input_tensors, &input_nodes);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  // Process outputs.