TF_Status* status);

// Set configuration key and value using coordination service.
// If coordination service is enabled, the key-value will be stored on the
// leader and become accessible to all workers in the cluster.
// Currently, a config key can only be set with one value, and subsequently
// setting the same key will lead to errors.
//

#         "nomsan",
#         "nosan",
#         "notsan",
#         "noubsan",
#     ],
#     deps = [":copybara_config"],
# )
#
# action_config_test(
#     name = "build_cleaner_spec_test",
#     src = "build_cleaner_spec.textproto",
# )
# copybara:uncomment_end

licenses(["notice"])

exports_files([
    "LICENSE",
    # The leakr files are used by //third_party/cloud_tpu and

    job_def->mutable_tasks()->insert(
        {i, tensorflow::strings::StrCat("localhost:", port)});
  }
  auto* config = server_def.mutable_default_session_config();
  config->mutable_experimental()->set_collective_group_leader(
      tensorflow::strings::StrCat("/job:", job_name, "/replica:0/task:", 0));
  auto* rewrite_options =
      config->mutable_graph_options()->mutable_rewrite_options();
  rewrite_options->set_scoped_allocator_optimization(

* `tf.lite`
    * Add experimental supports conversion of models that may be larger than 2GB before buffer deduplication

### Bug Fixes and Other Changes

* `tf.py_function` and `tf.numpy_function` can now be used as function decorators for clearer code:
   ```
   @tf.py_function(Tout=tf.float32)
   def my_fun(x):
     print("This always executes eagerly.")
     return x+1

# proto-file: devtools/build_cleaner/proto/actions.proto
# proto-message: ActionSpecs

# Python rules should not have more than one source file.
action_spec {
  action: CHECK_FILE_COUNT
  file_count_params {
    rule_selector {
      rule_kind_regex: "^.*py(type)?(_strict)?_(binary|library|test).*$"
      generator_function_regex: "^(?!boq_header)$"
    }
    max_source_count: 1
  }

  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a
  // CPU tensor to GPU).
  // Setting a larger thread pool does not help with the Swift caller, as we use
  // a different TFE context for each thread of execution (for running graph
  // functions, and their send/recvs corountines).

*   [TensorFlow Codelabs](https://codelabs.developers.google.com/?cat=TensorFlow)
*   [TensorFlow Blog](https://blog.tensorflow.org)
*   [Learn ML with TensorFlow](https://www.tensorflow.org/resources/learn-ml)
*   [TensorFlow Twitter](https://twitter.com/tensorflow)
*   [TensorFlow YouTube](https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ)