return;
  }
  status->status = coord_agent->DeleteKeyValue(key);
}

void TFE_ReportErrorToCluster(TFE_Context* ctx, int error_code,
                              const char* error_message, TF_Status* status) {
  tensorflow::ImmediateExecutionDistributedManager* dist_mgr =
      tensorflow::unwrap(ctx)->GetDistributedManager();
  tsl::CoordinationServiceAgent* coord_agent =

cff-version: 1.2.0
message: "If you use TensorFlow in your research, please cite it using these metadata. Software is available from tensorflow.org."
title: TensorFlow, Large-scale machine learning on heterogeneous systems

// Report error (specified by error_code and error_message) to other tasks in
// the cluster.
TF_CAPI_EXPORT extern void TFE_ReportErrorToCluster(TFE_Context* ctx,
                                                    int error_code,
                                                    const char* error_message,
                                                    TF_Status* status);

contribution and copyright details. If a contributor wants to further mark
their specific copyright on a particular contribution, they should indicate
their copyright solely in the commit message of the change when it is
committed.

LICENSE

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

    if (inputs.size() != parallel_device.num_underlying_devices()) {
      std::string message(absl::StrCat(
          "The parallel device ", parallel_device_name, " expected ",
          parallel_device.num_underlying_devices(),
          " inputs to TPUReplicatedInput, but got ", inputs.size()));
      TF_SetStatus(status, TF_INVALID_ARGUMENT, message.c_str());
      return result;
    }
    std::vector<TensorHandlePtr> components;

# 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
  }

      TF_SetStatus(first_bad_status.get(), TF_GetCode(async_wait_status.get()),
                   TF_Message(async_wait_status.get()));
    }
  }

  if (first_bad_status != nullptr) {
    TF_SetStatus(status, TF_GetCode(first_bad_status.get()),
                 TF_Message(first_bad_status.get()));
  }
}

absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>
ParallelDevice::Join(

    # Each bazel RBE invocation should produce two
    # 'Streaming build results to: ...' lines, one at the start, and one at the
    # end of the invocation.
    # If there's a failure message, it will be found in-between these two.

    if not current_url:
      url_lines['start'] = i
    elif current_url == url:
      url_lines['end'] = i
    else:
      result_store_links[current_url]['next_url'] = i

static inline void TF_SetStatusFromGCSStatus(
    const google::cloud::Status& gcs_status, TF_Status* status) {
  TF_SetStatus(status, static_cast<TF_Code>(gcs_status.code()),
               gcs_status.message().c_str());
}

static void* plugin_memory_allocate(size_t size) { return calloc(1, size); }
static void plugin_memory_free(void* ptr) { free(ptr); }

void ParseGCSPath(const std::string& fname, bool object_empty_ok,

 different computation approaches and orders.
    *   To verify if oneDNN optimizations are on, look for a message with *"oneDNN custom operations are on"* in the log. If the exact phrase is not there, it means they are off.

* Making the `tf.function` type system fully available: