arg->set_dtype(dtype);
    // TODO(lyandy): Support other arg kinds.
    if (dtype == tensorflow::DT_RESOURCE)
      arg->set_kind(tensorflow::tpu::TPUCompileMetadataProto::Arg::VARIABLE);
    else
      arg->set_kind(tensorflow::tpu::TPUCompileMetadataProto::Arg::PARAMETER);

    // Populate argument shapes.
    *arg->mutable_shape() = tensorflow::TensorShapeProto();

    arg_shapes.emplace_back(tensor_shape);

    DataType dtype;
    TF_RETURN_IF_ERROR(ConvertToDataType(input_type, &dtype));

    auto metadata_arg = metadata_proto.add_args();
    metadata_arg->set_kind(tpu::TPUCompileMetadataProto::Arg::PARAMETER);
    metadata_arg->set_dtype(dtype);
  }

  return absl::OkStatus();
}

absl::Status SetupReturnValues(mlir::ModuleOp module,

  std::vector<TensorShape> arg_shapes = {{1}};
  TPUCompileMetadataProto metadata_proto;
  auto arg = metadata_proto.add_args();
  arg->set_dtype(DataType::DT_FLOAT);
  arg->set_kind(TPUCompileMetadataProto::Arg::PARAMETER);
  metadata_proto.add_retvals();
  bool use_tuple_args = true;
  std::vector<ShardingAndIndex> arg_core_mapping;
  std::vector<std::vector<xla::Shape>> per_core_arg_shapes;

    // Set up an arg per arg_shape with the specified type.
    for (int i = 0; i < arg_shapes.size(); ++i) {
      auto metadata_arg = metadata_proto.add_args();
      metadata_arg->set_kind(
          tensorflow::tpu::TPUCompileMetadataProto::Arg::PARAMETER);
      metadata_arg->set_dtype(dtype);
    }
    // Set up one dummy retval.
    metadata_proto.add_retvals();
    bool use_tuple_args = true;

jacob-delgado <******@****.***> 1696273699 -0600

        this.kind = Preconditions.checkNotNull(kind);
        this.pattern = Preconditions.checkNotNull(pattern);
    }

    public String getKind() {
        return kind;
    }

    public void setKind(String kind) {
        this.kind = kind;
    }

    public String getPattern() {
        return pattern;
    }

    public void setPattern(String pattern) {

        String callableNameForDocComment = interceptedCallable.getKind() == CallableKindInfo.AFTER_CONSTRUCTOR ? className.simpleName() : interceptedCallable.getCallableName();
        List<ParameterInfo> params = request.getInterceptedCallable().getParameters();
        List<ParameterInfo> methodParameters = params.stream().filter(parameter -> parameter.getKind().isSourceParameter()).collect(Collectors.toList());

        }
    }

    private static boolean hasGroovyPropertyRequests(List<CallInterceptionRequest> requests) {
        return requests.stream().anyMatch(it -> it.getInterceptedCallable().getKind() == GROOVY_PROPERTY_GETTER || it.getInterceptedCallable().getKind() == GROOVY_PROPERTY_SETTER);
    }

    @SuppressWarnings("ReturnValueIgnored")
    private static CodeBlock interceptorClassJavadoc(Collection<CallInterceptionRequest> requests) {

        CodeBlock result = interceptedCallable.getKind() == CallableKindInfo.STATIC_METHOD ? CodeBlock.of("opcode == $T.INVOKESTATIC", Opcodes.class) :
            interceptedCallable.getKind() == CallableKindInfo.INSTANCE_METHOD ? CodeBlock.of("(opcode == $1T.INVOKEVIRTUAL || opcode == $1T.INVOKEINTERFACE)", Opcodes.class) :

For example, to create two clusters, run the script two times with the following
parameters:

```bash
  ./setupkind.sh --cluster-name cluster1 --ip-space 255 -i dual
  ./setupkind.sh --cluster-name cluster2 --ip-space 245 -i dual
```

To setup a dual-stack cluster that prefers IPv6 instead of IPv4 (the default)

```bash