type ActionID [HashSize]byte

// An OutputID is a cache output key, the hash of an output of a computation.
type OutputID [HashSize]byte

// Cache is the interface as used by the cmd/go.
type Cache interface {
	// Get returns the cache entry for the provided ActionID.
	// On miss, the error type should be of type *entryNotFoundError.
	//
	// After a success call to Get, OutputFile(Entry.OutputID) must

            output->DebugString(100));
}

REGISTER_OP("AllocateOutputOp0").Output("output1: float");

TEST_F(DeviceKernelOpTest, TestAllocateEmptyOutput) {
  auto my_compute_func = [](void* kernel, TF_OpKernelContext* ctx) {
    TF_Status* s = TF_NewStatus();
    // Allocate empty output
    int64_t dim = 0;
    TF_Tensor* output = TF_AllocateOutput(

  std::unordered_set<string> outputs;
  TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, &inputs,
                                &outputs));
  std::unordered_set<string> expected_inputs = {"input0:0", "input1:0"};
  std::unordered_set<string> expected_outputs = {"output0:0", "output1:0"};
  EXPECT_EQ(expected_inputs, inputs);
  EXPECT_EQ(expected_outputs, outputs);
}

        setup:
        sampleBuildWithTest()

        when:
        String output1 = runTaskAndTestClassUsing(firstDebug)
        String output2 = runTaskAndTestClassUsing(secondDebug)

        then:
        assertTestDebugMode(output1, firstDebug)
        assertTestDebugMode(output2, secondDebug)

        where:
        scenarioName                     | firstDebug | secondDebug

  TF_OpDefinitionBuilderAddInput(builder, "input1: uint8");
  TF_OpDefinitionBuilderAddInput(builder, "input2: uint32");
  TF_OpDefinitionBuilderAddOutput(builder, "output1: uint8");
  TF_OpDefinitionBuilderAddOutput(builder, "output2: uint8");
  TF_OpDefinitionBuilderSetShapeInferenceFunction(
      builder, &TF_ShapeInferenceContextSetUnknownShape);
  TF_Status* status = TF_NewStatus();

    Predicate* false_switch = predicate_factory_.MakeNotPredicate(true_switch);

    // Output 0 is alive iff all inputs are alive and the condition is false.
    input_preds.push_back(false_switch);
    SetPredicate(n, 0, predicate_factory_.MakeAndPredicate(input_preds),
                 should_revisit);
    input_preds.pop_back();

    // Output 1 is alive iff all inputs are alive and the condition is true.

    %outputs_1, %control_1 = tf_executor.island wraps "tf.TPUReplicatedInput"(%outputs_0) {N = 1 : i64, T = "tfdtype$DT_FLOAT", device = "", name = "input1"} : (tensor<0xf32>) -> tensor<0xf32>

  params_->body_outputs[1] = params_->body_inputs[1];

  // Finalize while loop
  ExpectOK();

  // Validate while loop outputs returned by TF_FinishWhile()
  EXPECT_TRUE(outputs_[0].oper != nullptr);
  EXPECT_GE(outputs_[0].index, 0);
  EXPECT_TRUE(outputs_[1].oper != nullptr);
  EXPECT_GE(outputs_[1].index, 0);

  // Check that cond and body inputs are not present
  for (int i = 0; i < params_->ninputs; ++i) {

      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:

		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)