return false;
        }
        Output output = (Output) o;
        return Objects.equal(path, output.path);
    }

    @Override
    public int hashCode() {
        return Objects.hashCode(path);
    }

    @Override
    public String toString() {
        return "Output{path='" + path + "'}";
    }

zirain <******@****.***> 1703405786 +0800

Jacek Ewertowski <******@****.***> 1701261434 +0100

		panic("FAIL")
	}
}

func dummyID(x int) [Size]interface{} {
	var out [Size]interface{}
	out[0] = x
	return out
}

const Size = 32

type OutputID [Size]interface{}

//go:noinline
func Get() OutputID {
	return dummyID(1234)

      [](const Scope& s, const std::vector<Output>& inputs, Output* output) {
        *output = ops::Less(s, inputs[0], 10);
        return s.status();
      },
      [](const Scope& s, const std::vector<Output>& inputs,
         std::vector<Output>* outputs) {
        outputs->push_back(ops::AddN(s, {inputs[0], inputs[1]}));
        outputs->push_back(ops::AddN(s, {inputs[1], 1}));
        outputs->push_back(inputs[2]);

  }

  func.func @mul1(%arg0: tensor<1xf32> {tf_saved_model.index_path = ["y"]}, %arg1: tensor<1xf32> {tf_saved_model.index_path = ["x"]}) -> (tensor<1xf32> {tf_saved_model.index_path = ["output_0"]}) attributes {tf.entry_function = {inputs = "mul1_y:0,mul1_x:0", outputs = "output:0"}, tf_saved_model.exported_names = ["mul1"]} {
    %0 = "tf.Mul"(%arg1, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    func.return %0 : tensor<1xf32>
  }

      type: DT_FLOAT
    }
  }
}
node {
  name: "output_0"
  op: "Identity"
  input: "conv_net_2d_1/Relu"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
library {
}

# CHECK: 'main' inputs:
# CHECK-NEXT: name: 'input'
# CHECK-NEXT: 'main' outputs:

  ResetOutputValues();
  outputs_.clear();
  for (TF_Operation* o : outputs) {
    outputs_.emplace_back(TF_Output{o, 0});
  }
  output_values_.resize(outputs_.size());
}

void CSession::SetOutputs(const std::vector<TF_Output>& outputs) {
  ResetOutputValues();
  outputs_ = outputs;
  output_values_.resize(outputs_.size());
}

      type: DT_FLOAT
    }
  }
}
node {
  name: "output_0"
  op: "Identity"
  input: "conv_net_2d_1/Relu"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
library {
}

# CHECK: 'main' inputs:
# CHECK-NEXT: name: 'input'
# CHECK-NEXT: 'main' outputs:

      %outputs_4, %control_5 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<""> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>