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(

      )

      inputs = {'input_vocabs': input_vocabs_placeholder}
      outputs = {
          'lookup': lookup_tensor,
          'output': output_tensor,
      }

      self._save_tf1_model(
          sess,
          output_path,
          signature_def_key,
          tags,
          inputs=inputs,
          outputs=outputs,
          init_op=lookup_ops.tables_initializer(),

        }

        and:
        outputs.stdOut.contains("BEFORE OUT")
        stripped(outputs.stdOut).contains(result1.output)
        stripped(outputs.stdOut).contains(result2.output)
        outputs.stdOut.contains("AFTER OUT")

        and:
        outputs.stdErr.contains("BEFORE ERR")
        stripped(outputs.stdOut).contains(result1.error)
        stripped(outputs.stdOut).contains(result2.error)

        %0 = tf_executor.graph {
          %output_0, %control_0 = tf_executor.island wraps "tf.Const"() <{value = dense<1.0> : tensor<1x2xf32>}> : () -> tensor<1x2xf32>

  func.func @conv_with_multiple_uses(%arg0: tensor<1x3x4x3xf32> {tf_saved_model.index_path = ["input"]}) -> (tensor<1x3x2x2xf32> {tf_saved_model.index_path = ["output"]}, tensor<1x3x2x1xf32> {tf_saved_model.index_path = ["output2"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input:0", outputs = "PartitionedCall:0, Sum:0"}, tf_saved_model.exported_names = ["serving_default"]} {

  func.func @serving_default(%arg0: tensor<1024x1024xf32> {tf_saved_model.index_path = ["input_tensor"]}) -> (tensor<1024x3xf32> {tf_saved_model.index_path = ["output1"]}, tensor<1024x3xf32> {tf_saved_model.index_path = ["output2"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input_tensor:0", outputs = "PartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    %0 = stablehlo.constant dense<1.000000e+03> : tensor<1024x3xf32>

  return options;
}

Status ClientSession::Run(const std::vector<Output>& fetch_outputs,
                          std::vector<Tensor>* outputs) const {
  return Run(FeedType{}, fetch_outputs, {}, outputs);
}

Status ClientSession::Run(const FeedType& inputs,
                          const std::vector<Output>& fetch_outputs,
                          std::vector<Tensor>* outputs) const {
  return Run(inputs, fetch_outputs, {}, outputs);
}

            apply plugin: "base"
            task customTask {
                inputs.file "input.txt"
                outputs.file "build/output" withPropertyName "output"
                outputs.cacheIf { true }
                doLast {
                  mkdir('build/output')
                  file('build/output/output.txt').text = file('input.txt').text
                }
            }
        """

        then:

        then:
        outputs.stdOut.readLines() == ['message']
        outputs.stdErr == ''
    }

    def rendersErrorLogEventsToStdErr() {
        when:
        renderer.attachSystemOutAndErr()
        renderer.onOutput(event('message', LogLevel.ERROR))

        then:
        outputs.stdOut == ''
        outputs.stdErr.readLines() == ['message']
    }

        [_AsClassName(x) for x in ctx.attr.srcs],
    )
    ctx.actions.write(output = ctx.outputs.out, content = _OUTPUT % (
        classes,
        ctx.attr.outname,
    ))

_GenSuite = rule(
    attrs = {
        "srcs": attr.label_list(allow_files = True),
        "outname": attr.string(),
    },
    outputs = {"out": "%{name}.java"},
    implementation = _impl,
)