return tx.Exec("DROP TABLE IF EXISTS ?", m.CurrentTable(stmt)).Error
		}); err != nil {
			return err
		}
	}
	return nil
}

// HasTable returns table exists or not for value, value could be a struct or string
func (m Migrator) HasTable(value interface{}) bool {
	var count int64

	m.RunWithValue(value, func(stmt *gorm.Statement) error {
		currentDatabase := m.DB.Migrator().CurrentDatabase()

    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>
    %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = i64, shared_name = "hash_table_dba2ccaa-f1b1-46d6-b276-98008f69da71", use_node_name_sharing = false, value_dtype = !tf_type.string} : () -> tensor<!tf_type.resource>

	SUB	R8,R9,R8

	// The last iteration is reduced in a separate step
	ADD	$-1,R7
	MOVD	R7,CTR

	// Determine which constant table (depends on poly)
	CMP	R14,$1
	BNE	castTable
	MOVD	$·IEEEConst(SB),R3
	BR	startConst
castTable:
	MOVD	$·CastConst(SB),R3

startConst:
	ADD	R3,R8,R3	// starting point in constants table

	VXOR	V0,V0,V0	// clear the V regs
	VXOR	V1,V1,V1
	VXOR	V2,V2,V2

  // CHECK: @legacy_init_op
  func.func @legacy_init_op(%arg0: tensor<!tf_type.string> {tf_saved_model.bound_input = @asset}) attributes {tf_saved_model.exported_names = ["f"]} {
    %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "hash_table_shared_name", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>

  let constructor = "CreateInsertCallOnceOpFromSessionInitializerPass()";
  let dependentDialects = ["TFL::TensorFlowLiteDialect"];
}

def LegalizeHashTablesPass : Pass<"tfl-legalize-hashtables-tf", "mlir::ModuleOp"> {
  let summary = "Legalize TensorFlow hash tables to TensorFlow Lite dialect.";
  let constructor = "CreateLegalizeHashTablesPass()";
  let dependentDialects = ["TFL::TensorFlowLiteDialect"];
}

//     consistent. For now, we require we know the current package
//     path so we can always expand symbol names. (Otherwise,
//     symbols with relocations are not considered hashable.)
//
// For now, we assume there is no circular dependencies among
// hashed symbols.
func (w *writer) contentHash(s *LSym) goobj.HashType {
	h := notsha256.New()
	var tmp [14]byte

    "PLACEHOLDER_FOR_GREATER_OP_CODES",
    "CUMSUM",
    "CALL_ONCE",
    "BROADCAST_TO",
    "RFFT2D",
    "CONV_3D",
    "IMAG",
    "REAL",
    "COMPLEX_ABS",
    "HASHTABLE",
    "HASHTABLE_FIND",
    "HASHTABLE_IMPORT",
    "HASHTABLE_SIZE",
    "REDUCE_ALL",
    "CONV_3D_TRANSPOSE",
    "VAR_HANDLE",
    "READ_VARIABLE",
    "ASSIGN_VARIABLE",

  );

  let results = (outs
    Res<TF_FloatTensor, [{`images` converted to RGB.}]>:$output
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_HashTableOp : TF_Op<"HashTable", []> {
  let summary = "Creates a non-initialized hash table.";

  let description = [{
This op creates a hash table, specifying the type of its keys and values.

  return IdentityArithmeticOpFolder<MulOp>(*this, operands);
}

//===----------------------------------------------------------------------===//
// HashTableOp
//===----------------------------------------------------------------------===//
void HashTableOp::getCanonicalizationPatterns(RewritePatternSet& results,
                                              MLIRContext* context) {

    // expected-remark@above {{Successors: {6}}}
        %handle1 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "some_name", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>
        // expected-remark@above {{ID: 0}}