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());
}

  %graph_result = tf_executor.graph {
    %output0, %control0 = tf_executor.island {
      %a = "tf.opA"(%arg0) : (tensor<i32>) -> tensor<i32>
      tf_executor.yield %a : tensor<i32>
    }
    %output1, %control1 = tf_executor.island {
      %b = "tf.opB"(%output0) : (tensor<i32>) -> tensor<i32>
      tf_executor.yield %b : tensor<i32>
    }
    tf_executor.fetch %output1 : tensor<i32>
  }

                @Override
                void transform(TransformOutputs outputs) {
                    def input = inputArtifact.get().asFile
                    def output1 = outputs.file(input.name + ".1." + target)
                    def output2 = outputs.file(input.name + ".2." + target)
                    if (showOutput) {

        withWatchFs().run ":incremental", "-DoutputDir=output1"
        then:
        executedAndNotSkipped(":incremental")

        when:
        file("build/output2/overlapping.txt").text = "overlapping"
        waitForChangesToBePickedUp()
        withWatchFs().run ":incremental", "-DoutputDir=output2"
        then:
        executedAndNotSkipped(":incremental")
        file("build/output1").assertDoesNotExist()

        when:

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-unconditionally-use-set-output-shapes-on-import -tf-enable-shape-inference-on-import=false -tf-graph-as-function %s -o -  | FileCheck %s
# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-unconditionally-use-set-output-shapes-on-import -tf-enable-shape-inference-on-import=true -tf-graph-as-function %s -o - | FileCheck %s

# Verify importing with _output_shapes enabled works as expected.

node {
  name: "_Arg"

    tf_executor.fetch %outputs_2#1, %outputs_4#1, %outputs_6#1 : tensor<5xf32>, tensor<5xf32>, tensor<5xf32>
  }
  func.return %0#0, %0#1, %0#2 : tensor<5xf32>, tensor<5xf32>, tensor<5xf32>
}

func.func @cond(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>) -> tensor<i1> {
  %0 = tf_executor.graph {

Benjamin Elder <******@****.***> 1547627953 -0800

Laura Kassovic <******@****.***> 1701107622 -0800

    }

    private static String[] customTaskWithOutputs(List<String> outputs) {
        (["customTask", "-PnumOutputs=${outputs.size()}"] + outputs.withIndex().collect { value, idx -> "-Poutput${idx}" + (value ? "=${value}" : '') }) as String[]
    }

    def "output files moved from one location to another marks task up-to-date"() {
        buildFile << """