status.ForEachPayload(
      [tf_status](absl::string_view key, const absl::Cord& value) {
        std::string key_str(key);
        std::string value_str(value);
        TF_SetPayload(tf_status, key_str.c_str(), value_str.c_str());
      });
}

absl::Status StatusFromTF_Status(const TF_Status* tf_status) {
  absl::Status status(StatusCodeFromTSLCode(TF_GetCode(tf_status)),
                      TF_Message(tf_status));

    // Print tensor result.
    fprintf(stdout, "Tensor<type: %s, shape: %s, values: %s>\n",
            TfLiteTypeGetName(out.type), TfLiteTensorDimString(out).c_str(),
            TfLiteTensorString(out).c_str());
  }

  if (dump_state) tflite::PrintInterpreterState(interpreter.get());

  return 0;

			// Parse line.
			if len(parts) != 3 {
				log.Fatalf(`%s:%d: invalid symabi: syntax is "%s sym abi"`, file, lineNum, parts[0])
			}
			sym, abistr := parts[1], parts[2]
			abi, valid := obj.ParseABI(abistr)
			if !valid {
				log.Fatalf(`%s:%d: invalid symabi: unknown abi "%s"`, file, lineNum, abistr)
			}

			sym = s.canonicalize(sym)

			// Record for later.
			if parts[0] == "def" {
				s.defs[sym] = abi
			} else {

    }

    def "base version includes the first . separated parts"() {
        expect:
        def version = parse(versionStr)
        version.baseVersion == parse(baseStr)
        version.qualified == qualified

        where:
        versionStr        | baseStr     | qualified
        "1.2.3"           | "1.2.3"     | false
        "1.2-3"           | "1.2"       | true
        "1.2-beta_3+0000" | "1.2"       | true

  const std::string module_path =
      tensorflow::GetDataDependencyFilepath(kSimpleModel);

  TF_ASSERT_OK_AND_ASSIGN(Object module,
                          runtime.Load(String(module_path.c_str())));
  std::cout << "Module imported." << std::endl;

  TF_ASSERT_OK_AND_ASSIGN(Callable fn,
                          module.Get<Callable>(String("test_float")));
  TF_ASSERT_OK_AND_ASSIGN(

  TFE_OpSetAttrString(op, "tensor_name", "dummy", 5);
  TFE_OpSetAttrString(op, "send_device", send_device.c_str(),
                      send_device.size());
  TFE_OpSetAttrString(op, "recv_device", recv_device.c_str(),
                      recv_device.size());
  TFE_OpSetAttrInt(op, "send_device_incarnation", send_device_incarnation);

  return op;
}

			"", // account-id is always empty in this implementation
			arn.ResourceType + "/" + arn.ResourceID,
		},
		":",
	)
}

// Parse - parses an ARN string into a type.
func Parse(arnStr string) (arn ARN, err error) {
	ps := strings.Split(arnStr, ":")
	if len(ps) != 6 || ps[0] != string(arnPrefixArn) {
		err = errors.New("invalid ARN string format")
		return
	}

	if ps[1] != string(arnPartitionMinio) {

    grad_inputs[0]->Ref();

    // Grad for bias
    std::string name = "bias_add_grad";
    TF_RETURN_IF_ERROR(BiasAddGrad(ctx, upstream_grad, &grad_inputs[1],
                                   data_format.c_str(), name.c_str()));

    return absl::OkStatus();
  }
  ~BiasAddGradientFunction() override {}

 private:
  AttrBuilder forward_attrs_;
};

}  // namespace

    node_name = std::string(attr.getValue());
  }
  auto node_def_or = ConvertTFDialectOpToNodeDef(
      inst, node_name.c_str(), /*ignore_unregistered_attrs=*/true);
  RETURN_FAILURE_IF_ERROR(node_def_or.status());
  const auto& node_def = node_def_or.value();

  TFE_Op* op = TFE_NewOp(context, node_def->op().c_str(), status);
  RETURN_FAILURE_IF_ERROR(status);
  auto clean_op = MakeCleanup([op] { TFE_DeleteOp(op); });

          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;
    components.reserve(inputs.size());
    for (int i = 0; i < inputs.size(); ++i) {