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:

    output = project.file("output.txt")
}

task fileCollectionTextUsingTaskProvider(type: MyTask) {
    config = resources.text.fromFile(generateConfigFile)
    output = project.file("output.txt")
}

task archiveEntryText(type: MyTask) {
    config = resources.text.fromArchiveEntry(configurations.sharedConfig, "config.txt")
    output = project.file("output.txt")

        modifyOutputs | incremental | description
        "add"         | false       | "with additional outputs is fully rebuilt"
        "change"      | false       | "with changed outputs is fully rebuilt"
        "remove"      | false       | "with removed outputs is fully rebuilt"
        "none"        | true        | "with unmodified outputs is executed as incremental"
    }

namespace ops {

// Function that takes cond graph inputs and returns cond graph boolean output.
// 'output' need not be set if an error is returned.
typedef std::function<Status(const Scope&, const std::vector<Output>& inputs,
                             Output* output)>
    CondGraphBuilderFn;

// Function that takes body graph inputs and returns body graph outputs.
// 'outputs' need not be populated if an error is returned.

  ClientSession session(scope);
  TF_CHECK_OK(session.Run(tensors, out));
}

void GetTensor(const Scope& scope, Output tensor, Tensor* out) {
  std::vector<Tensor> outputs;
  GetTensors(scope, {std::move(tensor)}, &outputs);
  *out = outputs[0];
}

void GetTensors(const Scope& scope, const std::vector<Output>& assign_vars,
                const OutputList& tensors, std::vector<Tensor>* out) {
  ClientSession session(scope);

        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']
    }

                const OutputList& tensors, std::vector<Tensor>* out);

/// Computes the output 'tensor', returning the resulting tensor in 'out'.
void GetTensor(const Scope& scope, Output tensor, Tensor* out);

// Computes the output 'tensor', returning the resulting tensor in 'out'.
// assign_vars are extra outputs that should be run
// e.g. to assign values to variables.