```
--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

    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`

  const std::string dirpath = GetURIForPath("dir");
  Status status = env_->CreateDir(dirpath);
  if (!status.ok()) GTEST_SKIP() << "CreateDir() not supported: " << status;

  // If updating, make sure to update expected_children below.
  const std::vector<std::string> filenames = {
      GetURIForPath("dir/a_file"),
      GetURIForPath("dir/another_file"),
  };

    // This modification only updates the destination node for
    // the purposes of running this graph in a session. Thus, we don't
    // record the source node as being modified.
    RecordMutation(graph, *dst.oper, "updating input tensor");
  }
}

// Apis that are corresponding to python c api. --------------------------

void TF_AddOperationControlInput(TF_Graph* graph, TF_Operation* op,

  TestRemoteExecuteUpdateServerDefResourceAccess(true);
}

void TestRemoteExecuteUpdateServerDefWithFailures(bool async) {
  // Fail fast on GetStatus requests so we can get errors instead of timeout
  // when updating cluster with non-exsitent worker
  tensorflow::setenv("GRPC_FAIL_FAST", "TRUE", /*overwrite=*/1);

  tensorflow::ServerDef server_def = GetServerDef(2);
  // This server def has the task index set to 0.

  status->status = absl::OkStatus();
  return tensorflow::wrap(tensorflow::TensorHandle::CreateLocalHandle(tensor));
}

// Set server_def on the context, possibly updating it.
TF_CAPI_EXPORT extern void TFE_EnableCollectiveOps(TFE_Context* ctx,
                                                   const void* proto,
                                                   size_t proto_len,

  return l;
}

void TFE_ContextClearCaches(TFE_Context* ctx) {
  tensorflow::unwrap(ctx)->ClearCachesAndThreadExecutors();
}

// Set server_def on the context, possibly updating it.
TF_CAPI_EXPORT extern void TFE_ContextSetServerDef(TFE_Context* ctx,
                                                   int keep_alive_secs,
                                                   const void* proto,

## Keras

*  `keras.layers.experimental.DynamicEmbedding`
    * Added `DynamicEmbedding` Keras layer
    * Added 'UpdateEmbeddingCallback`
    * `DynamicEmbedding` layer allows for the continuous updating of the
      vocabulary and embeddings during the training process. This layer
      maintains a hash table to track the most up-to-date vocabulary based on