TransformWorkspaceIdentity transformWorkspaceIdentity,
            Transform transform,
            ComponentIdentifier componentIdentifier
        ) {
            this.inputArtifact = inputArtifact;
            this.transformWorkspaceIdentity = transformWorkspaceIdentity;
            this.transform = transform;
            this.componentIdentifier = componentIdentifier;
        }

        @Override

		}
		if apiErr.HTTPStatusCode == http.StatusOK {
			return nil
		}
		return apiErr
	}
	return nil
}

// GetObjectLamdbaHandler - GET Object with transformed data via lambda functions
// ----------
// This implementation of the GET operation applies lambda functions and returns the
// response generated via the lambda functions. To use this API, you must have READ access

     * Unsatisfiable Request` response will be returned.
     */
    fun onlyIfCached() = commonOnlyIfCached()

    /** Don't accept a transformed response. */
    fun noTransform() = commonNoTransform()

    fun immutable() = commonImmutable()

    /**
     * Sets the maximum age of a cached response. If the cache response's age exceeds [maxAge], it

        // Keep track of transformed artifacts as a given artifact may appear in multiple variants and configurations
        Map<LocalComponentArtifactMetadata, LocalComponentArtifactMetadata> transformedArtifacts = new HashMap<>();

                            }
                        }
                    }
                }
            }
        }
    }

    @TargetGradleVersion(">=8.8")
    def "reports transformed display names with DisplayNameGeneration"() {
        file("src/test/java/org/example/ComplexTests.java") << """package org.example;

import org.junit.jupiter.api.*;
import org.junit.jupiter.params.ParameterizedTest;

#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/target_hardware.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

namespace mlir {
namespace TFL {
namespace tac {
namespace {

    ```mlir
      %0 = "tf.ReadVariableOp"(%arg0)
      %1 = "tf.ReadVariableOp"(%arg1)
      %2 = "tf.TPUExecute"(%0, %1, %compile)
      %3 = "tf.AssignVariableOp"(%arg0, %2)
    ```

    will be transformed into

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

    if (failed((isQuantizableWeight(op)))) {
      return failure();
    }

    // 2. Quantize the constant to the provided data type.
    // After quantization, the graph will be transformed
    // from:
    // const -> some op -> quantizable_op
    // to:
    // q_const -> dequant_op -> some op -> quantizable_op
    //
    // A dequant_op will propagate to further quantize the next ops in another

	return func() (State, error) {
		transformer, encObject, cacheKey, errGen := GenerateTransformer(ctx, string(uuid.NewUUID()), envelopeService, useSeed)
		if errGen != nil {
			return State{}, errGen
		}
		return State{
			Transformer:         transformer,
			EncryptedObject:     *encObject,
			UID:                 "panda",

struct TrivialTransformInfo {
  // Can the op be transformed trivially?
  bool can_transform = false;

  // List of callee names (one for each region).
  llvm::SmallVector<StringRef, 2> callee_names;

  // Analyzes the given calls (from regions attached to the same parent op) to
  // check if the parent op be transformed to functional form trivially (i.e.,