Attribute inputSpec = std::get<1>(input);
    if (!isValidQuantizationSpec(inputSpec, inputType)) {
      return emitOpError() << "has incompatible specification " << inputSpec
                           << " and input type " << inputType;
    }
  }

  for (auto result : llvm::zip(getResultTypes(), getOutputSpecs())) {
    Type outputType = std::get<0>(result);
    Attribute outputSpec = std::get<1>(result);

    Attribute inputSpec = std::get<1>(input);
    if (!isValidQuantizationSpec(inputSpec, inputType)) {
      return emitOpError() << "has incompatible specification " << inputSpec
                           << " and input type " << inputType;
    }
  }

  for (auto result : llvm::zip(getResultTypes(), getOutputSpecs())) {
    Type outputType = std::get<0>(result);
    Attribute outputSpec = std::get<1>(result);

        // Once we migrate to all-scheduled transforms we should consider if we can avoid having this optimization and use only normalized inputs.
        //
        // Note that we are not capturing this value in the actual inputs of the work; doing so would cause unnecessary cache misses.
        // This is why the hash is captured here and not in visitIdentityInputs().

  int input_index;
  int output_index;
};

// Idenitfy tf_device.cluster_func input-output alias pairs.
// This is currently conservative, primarily handling the following base case:
// ```
// %value = tf.ReadVariableOp(%resource_var)
// %output:N = tf_device.cluster_func(..., /*input index = a*/ %value, ...)
// tf.AssignVariableOp(%resource_var, %output#b) // write output #b to resource
// ```

Once a task is added to a group, it is visible when listing tasks.

== Task input and outputs

For the task to do useful work, it typically needs some *inputs*.
A task typically produces *outputs*.

.build.gradle.kts
[source,kotlin]
----
abstract class CreateFileTask : DefaultTask() {
    @Input
    val fileText = "HELLO FROM MY TASK"

    @Input
    val fileName = "myfile.txt"

    @OutputFile

// RUN: tf-opt %s -tf-device-mark-input-output-aliases | FileCheck %s

// The following tests check if the aliasing pairs are conservatively marked
// correctly. In the following tests tf_device.cluster_func has inputs
// coming from ReadVariableOp and outputs written to a resource using
// AssignVariableOp. If a pair of input-output (say input at index `a` and
// output at index `b`) read and write to the same resource, then those
// input-output pairs alias each other.

One such optimization is called _incremental builds_.

An _incremental build_ is a build that avoids running tasks whose inputs did not change since the previous build, making the execution of such tasks unnecessary.

For incremental builds to work, tasks must define their inputs and outputs. At build time, Gradle will determine whether the input or outputs have changed. If they have changed, Gradle will execute the task. Otherwise, it will skip execution.

*/
----

The task then looks for files with the extension `.java` and adds a license header.

The task has a single input, the license file name, annotated with `@Input`.

Gradle uses the `@Input` annotation to determine if the task needs to run.
If the task has not run before or if the input value has changed since the previous execution, then Gradle will execute the task.

            try {
                String location = System.getProperty("input.file");
                BufferedReader reader = new BufferedReader(new FileReader(location));
                String input = reader.readLine();
                reader.close();
                FileWriter out = new FileWriter(args[args.length - 1], false);
                out.write(input);
                out.close();
            } catch (IOException e) {

// Returns the tf.Equal/tf.NotEqual result type given `x` and `y` and inputs. If
// `incompatible_shape_error` is true, reports error if `x` and `y` has
// incompatible shapes. Otherwise, returns a tensor type with unknown rank.
Type DeduceEqualCmpOpType(Builder *builder, Location loc, Value x, Value y,
                          BoolAttr incompatible_shape_error);

Type InferReductionOpType(Value input, Value reduction_indices,