func (f *Selector) Selects(inputs Set) bool {
	return !inputs.containsAny(f.absent) && inputs.containsAll(f.present)
}

// Excludes returns false, if the given set of labels, even combined with new ones, could end up satisfying the Selector.
// It returns false, if Matches would never return true, even if new labels are added to the input set.
func (f *Selector) Excludes(inputs Set) bool {
	return inputs.containsAny(f.absent)
}

// tf.function with 0 inputs
StructuredValue ZeroArgInputSignature();

// Returns a StructuredValue corresponding to the serialized InputSignature of a
// tf.function with 1 input
StructuredValue SingleArgInputSignature();

// Returns a StructuredValue corresponding to the serialized InputSignature of a
// tf.function with 3 inputs
StructuredValue ThreeArgInputSignature();

      cost += GetRankedTensorSize(type);
    } else {
      // For unranked tensors, use the max size among the input tensors. This is
      // because the only dynamic information of the function should be the
      // input, so the size of dynamic tensors should be usually capped by
      // inputs' sizes.
      cost += max_arg_size_;
    }
  }

  cost_map_[op] = cost;
}

}  // namespace tfrt_compiler

            getName() >> "input"
        }

        when:
        binary.sources.put("custom", customSourceSet)

        then:
        binary.sources.values()*.name == ["custom"]

        when:
        binary.inputs.add inputSourceSet

        then:
        binary.sources.values()*.name == ["custom"]
        binary.inputs*.name == ["input"]
    }

  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"]} {

     * and is expected to return the desired name for the corresponding Gradle task.
     * The transformer may be called multiple times with the same input.
     * Implementations should ensure uniqueness of the return value for a distinct input.
     * That is, no two inputs should yield the same return value.
     *

        buildFile << """
// This is a workaround to bust the JVM's file canonicalization cache
def f = file("delete-me")
f.createNewFile()
f.delete() // invalidates cache

task work {
    inputs.file('in.txt')
    inputs.dir('in-dir')
    def outTxt = file('out.txt')
    def outDir = file('out-dir')
    outputs.file(outTxt)
    outputs.dir(outDir)
    doLast {
        outTxt.text = 'content'

using llvm::cl::opt;

// Import options.
// NOLINTNEXTLINE
opt<std::string> input_arrays(
    "tf-input-arrays", llvm::cl::desc("Input tensor names, separated by ','"),
    llvm::cl::init(""));

// NOLINTNEXTLINE
opt<std::string> input_dtypes(
    "tf-input-data-types",
    llvm::cl::desc("(Optional) Input tensor data types, separated by ','. Use "
                   "'' if a single data type is skipped. The data type from "

        def ex = thrown(IllegalArgumentException)
        ex.message == "Unable to calculate percentage: 50 of -10. All inputs must be >= 0"

        when:
        percentOf(-1, 100) == 0
        then:
        ex = thrown(IllegalArgumentException)
        ex.message == "Unable to calculate percentage: -1 of 100. All inputs must be >= 0"
    }

    def "formats bytes (#bytes -> #humanReadableString)"(long bytes, String humanReadableString) {

            task producer {
                def inputTxt = file("input.txt")
                def outputTxt = file("build/output.txt")
                inputs.files(inputTxt)
                outputs.file(outputTxt)
                doLast {
                    outputTxt.text = inputTxt.text
                }
            }
        """

        file("input.txt").text = "input"
        def outputFile = file("build/output.txt")