return builder->create<TF::TPUCopyWithLayoutOp>(
      execute_launch.getLoc(), llvm::ArrayRef<Type>{input.getType()},
      llvm::ArrayRef<Value>{input, get_layout.getLayout()});
}

// Performs transformation for a non-replicated input.
void HandleInput(Value input, const int64_t execute_arg_index,
                 TF::TPUExecuteOp execute, tf_device::LaunchOp execute_launch,
                 tf_device::LaunchOp compile_launch) {

  // this class) and the all the calls match using the given argument matcher.
  //
  // If such a trivial transformation is possible, stash the relevant
  // information needed for the transformation, else indicate that a trivial
  // transformation is not possible by setting `can_transform` to false.
  TrivialTransformInfo(std::optional<func::CallOp> first_call,

        succeeds("showBuildscript")
        // A jar coming from some file repo is copied into the transformation cache and served from there.
        inArtifactTransformCache("test-1.3-BUILD-SNAPSHOT.jar")
        // A jar coming from remote repo is cached in the global modules cache and served from there.
        // It isn't copied into the transformation cache.
        // The transformed counterparts are not visible when printing classpath data.

		&metrics.HistogramOpts{
			Namespace:      namespace,
			Subsystem:      subsystem,
			Name:           "dek_cache_inter_arrival_time_seconds",
			Help:           "Time (in seconds) of inter arrival of transformation requests.",
			StabilityLevel: metrics.ALPHA,
			Buckets:        metrics.ExponentialBuckets(60, 2, 10),
		},
		[]string{"transformation_type"},
	)

	// These metrics are made public to be used by unit tests.

	// receive all of the transformed bytes.
	ErrShortDst = errors.New("transform: short destination buffer")

	// ErrShortSrc means that the source buffer has insufficient data to
	// complete the transformation.
	ErrShortSrc = errors.New("transform: short source buffer")

	// ErrEndOfSpan means that the input and output (the transformed input)
	// are not identical.

	// receive all of the transformed bytes.
	ErrShortDst = errors.New("transform: short destination buffer")

	// ErrShortSrc means that the source buffer has insufficient data to
	// complete the transformation.
	ErrShortSrc = errors.New("transform: short source buffer")

	// ErrEndOfSpan means that the input and output (the transformed input)
	// are not identical.

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// This transformation pass applies quantization on TFLite dialect.

#include <cstddef>
#include <string>
#include <utility>

#include "absl/container/flat_hash_set.h"
#include "llvm/ADT/STLExtras.h"

      %2 = "tf.TPUExecuteAndUpdateVariables"(%arg0, %arg1, %compile)
        { device_var_reads_indices = [0, 1],
          device_var_updates_indices = [0, -1] }
    ````

    The transformation happens only for on-device variables. The above
    transformation requires `%arg0`, `%arg1` to have the same device assignment
    as the `TPUExecute` op.
  }];

  let dependentDialects = [
    "mhlo::MhloDialect",

    // pass.
    //
    // Note that a constant can be used by multiple ops. For example, if a graph
    // looks like below:
    // const -> while -> quant_op
    //       -> not_quant_op
    //
    // the transformation will be:
    // q_const -> dequant_op -> while -> quant_op
    //                       -> not_quant_op
    // And the dequant_op op will propagate towards quant_op only.

  ];
}

def ModifyIONodesPass : Pass<"tfl-modify-io-nodes", "mlir::func::FuncOp"> {
  let summary = "Modify the type of the model io nodes";
  let description = [{
      This transformation pass modifies the input and output types of the function
      to what are specified. The task was not just adding cast operations, but,
      instead, using tfl.quantize and tfl.dequantize ops to scale the tensors.
  }];