rng.uniform(low=0.0, high=1.0, size=static_input_shape).astype(
            np.float32
        )
    )

    def data_gen() -> repr_dataset.RepresentativeDataset:
      for _ in range(100):
        yield {
            'input_tensor': rng.uniform(
                low=0.0, high=1.0, size=static_input_shape
            ).astype(np.float32)
        }

    dataset_path = self.create_tempfile('tfrecord').full_path

module {
func.func @simpleWhile(%arg0: tensor<i32>) -> tensor<i32> {
  %0 = "tfl.while"(%arg0) ({
  ^bb0(%block: tensor<i32>):
    "tfl.yield"(%block) : (tensor<i32>) -> ()
  },{
  ^bb0(%block: tensor<i32>):
    "tfl.yield"(%block) : (tensor<i32>) -> ()
  }) {tac.device = "CPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (tensor<i32>) -> tensor<i32>
  func.return %0 : tensor<i32>
}

    }
  }];
}

def TF_YieldOp : TF_Op<"Yield",
      [Terminator,
       Pure,
       NativeOpTrait<"ReturnLike", [], "", "">,
       ParentOneOf<["CaseRegionOp", "IfRegionOp", "WhileRegionOp", "GeneratorDatasetRegionOp"]>,
       DeclareOpInterfaceMethods<RegionBranchTerminatorOpInterface,
           ["getMutableSuccessorOperands"]>,
      ]> {
  let summary = "Yield operation";

  let description = [{

		c := field.Name[0]
		if 'a' <= c && c <= 'z' || c == '_' {
			panic("reflect.StructOf: field \"" + field.Name + "\" is unexported but missing PkgPath")
		}
	}

	resolveReflectType(field.Type.common()) // install in runtime
	f := structField{
		Name:   newName(field.Name, string(field.Tag), field.IsExported(), field.Anonymous),
		Typ:    field.Type.common(),
		Offset: 0,
	}
	return f, field.PkgPath
}

          /*device_ordinal=*/builder.getI64IntegerAttr(0), device_type_attr),
      builder);
}

// Clones an IfRegionOp 'if_region' and attributes and creates then/else regions
// with yield op and an empty block.
mlir::TF::IfRegionOp CloneEmptyIfWithPredicate(mlir::TF::IfRegionOp if_region,
                                               OpBuilder& builder) {

// profile that event by storing a pointer to the lock in its "to be profiled
// upon unlock" field. If that field is already set, it uses the relative
// magnitudes to weight a random choice between itself and the other lock, with
// the loser's time being added to the "additional contention" field. Otherwise
// if the M's call stack buffer is occupied, it does the comparison against that
// sample's magnitude.
//

  region.push_back(new mlir::Block);
  auto saved_pos = op_builder.saveInsertionPoint();
  op_builder.setInsertionPointToEnd(&region.front());
  mlir::Operation* cloned_op = op_builder.clone(*op);
  // Add the yield operation.
  op_builder.create<mlir::TFL::YieldOp>(op_loc, cloned_op->getResults());
  // Now emit into the function body again.
  op_builder.restoreInsertionPoint(saved_pos);

		// is the respective default type.
		check.updateExprType(x.expr, Default(x.typ), true)
		check.updateExprType(y.expr, Default(y.typ), true)
	}

	// spec: "Comparison operators compare two operands and yield
	//        an untyped boolean value."
	x.typ = Typ[UntypedBool]
	return

Error:
	// We have an offending operand errOp and possibly an error cause.
	if cause == "" {

    %7 = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg1, %arg2, %arg3) {device = "", narrow_range = false, num_bits = 8 : i64} : (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    "tfl.yield"(%7) : (tensor<*xf32>) -> ()
  }) {device = "", narrow_range = false, num_bits = 8 : i64} : (tensor<3x4xf32>, tensor<4xf32>, tensor<4xf32>) -> tensor<3x4xf32>

		// is the respective default type.
		check.updateExprType(x.expr, Default(x.typ), true)
		check.updateExprType(y.expr, Default(y.typ), true)
	}

	// spec: "Comparison operators compare two operands and yield
	//        an untyped boolean value."
	x.typ = Typ[UntypedBool]
	return

Error:
	// We have an offending operand errOp and possibly an error cause.
	if cause == "" {