%1 = "tf.LookupTableSizeV2"(%0) {device = ""} : (tensor<!tf_type.resource>) -> tensor<i64>
  // CHECK-LABEL: hashtable_size
  // CHECK-NEXT:  [[HASH_TABLE:%.*]] = "tfl.hashtable"() <{key_dtype = !tf_type.string, table_id = -1323619995 : i32, value_dtype = i64}> : () -> tensor<1x!tf_type.resource>
  // CHECK-NEXT:  [[SIZE:%.*]] = "tfl.hashtable_size"([[HASH_TABLE]]) : (tensor<1x!tf_type.resource>) -> tensor<i64>
  // CHECK-NEXT:  return [[SIZE]] : tensor<i64>

  CALL_ONCE = 129,
  BROADCAST_TO = 130,
  RFFT2D = 131,
  CONV_3D = 132,
  IMAG=133,
  REAL=134,
  COMPLEX_ABS=135,
  HASHTABLE = 136,
  HASHTABLE_FIND = 137,
  HASHTABLE_IMPORT = 138,
  HASHTABLE_SIZE = 139,
  REDUCE_ALL = 140,
  CONV_3D_TRANSPOSE = 141,
  VAR_HANDLE = 142,
  READ_VARIABLE = 143,
  ASSIGN_VARIABLE = 144,
}
// LINT.ThenChange(nnapi_linter/linter.proto)

  CALL_ONCE = 129,
  BROADCAST_TO = 130,
  RFFT2D = 131,
  CONV_3D = 132,
  IMAG=133,
  REAL=134,
  COMPLEX_ABS=135,
  HASHTABLE = 136,
  HASHTABLE_FIND = 137,
  HASHTABLE_IMPORT = 138,
  HASHTABLE_SIZE = 139,
  REDUCE_ALL = 140,
  CONV_3D_TRANSPOSE = 141,
  VAR_HANDLE = 142,
  READ_VARIABLE = 143,
  ASSIGN_VARIABLE = 144,
  BROADCAST_ARGS = 145,
  RANDOM_STANDARD_NORMAL = 146,
  BUCKETIZE = 147,

    TFL_TensorOf<[I32, TFL_Str, I64]>:$keys,
    TFL_TensorOf<[F32, I32, TFL_Str, I64]>:$values
  );

  let results = (outs);
}


def TFL_HashtableSizeOp: TFL_Op<"hashtable_size", []> {
  let summary = "Computes the number of elements in the given table.";

  let arguments = (ins
    TFL_ResourceTensor:$hash_table
  );

  let results = (outs
    TFL_I64Tensor:$out

    "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",
    "BROADCAST_ARGS",
    "RANDOM_STANDARD_NORMAL",
    "BUCKETIZE",