%17 = "tfl.reduce_prod"(%16, %cst_14) {keep_dims = true, tac.device = "CPU", tac.inference_type = "FLOAT"} : (tensor<2xi32>, tensor<1xi32>) -> tensor<1xi32>

  TILE = 69,
  EXPAND_DIMS = 70,
  EQUAL = 71,
  NOT_EQUAL = 72,
  LOG = 73,
  SUM = 74,
  SQRT = 75,
  RSQRT = 76,
  SHAPE = 77,
  POW = 78,
  ARG_MIN = 79,
  FAKE_QUANT = 80,
  REDUCE_PROD = 81,
  REDUCE_MAX = 82,
  PACK = 83,
  LOGICAL_OR = 84,
  ONE_HOT = 85,
  LOGICAL_AND = 86,
  LOGICAL_NOT = 87,
  UNPACK = 88,
  REDUCE_MIN = 89,
  FLOOR_DIV = 90,
  REDUCE_ANY = 91,

}

func.func @reduce_prod(%arg0: tensor<8x16x16xf32>, %arg1: tensor<2xi32>) -> tensor<?xf32> {
  %0 = "tf.Prod"(%arg0, %arg1) {keep_dims = false} : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32>
  func.return %0 : tensor<?xf32>

  // CHECK-LABEL: reduce_prod
  // CHECK: "tfl.reduce_prod"(%arg0, %arg1) <{keep_dims = false}> : (tensor<8x16x16xf32>, tensor<2xi32>) -> tensor<?xf32>

  TILE = 69,
  EXPAND_DIMS = 70,
  EQUAL = 71,
  NOT_EQUAL = 72,
  LOG = 73,
  SUM = 74,
  SQRT = 75,
  RSQRT = 76,
  SHAPE = 77,
  POW = 78,
  ARG_MIN = 79,
  FAKE_QUANT = 80,
  REDUCE_PROD = 81,
  REDUCE_MAX = 82,
  PACK = 83,
  LOGICAL_OR = 84,
  ONE_HOT = 85,
  LOGICAL_AND = 86,
  LOGICAL_NOT = 87,
  UNPACK = 88,
  REDUCE_MIN = 89,
  FLOOR_DIV = 90,
  REDUCE_ANY = 91,

  TILE = 69,
  EXPAND_DIMS = 70,
  EQUAL = 71,
  NOT_EQUAL = 72,
  LOG = 73,
  SUM = 74,
  SQRT = 75,
  RSQRT = 76,
  SHAPE = 77,
  POW = 78,
  ARG_MIN = 79,
  FAKE_QUANT = 80,
  REDUCE_PROD = 81,
  REDUCE_MAX = 82,
  PACK = 83,
  LOGICAL_OR = 84,
  ONE_HOT = 85,
  LOGICAL_AND = 86,
  LOGICAL_NOT = 87,
  UNPACK = 88,
  REDUCE_MIN = 89,
  FLOOR_DIV = 90,
  REDUCE_ANY = 91,

  %cst_1 = arith.constant dense<[1, 1]> : tensor<2xi32>
  %0 = "tfl.reduce_prod"(%arg0, %cst) {keep_dims = false} : (tensor<8x128xf32>, tensor<2xi32>) -> tensor<f32>
  %1 = "tfl.reshape"(%0, %cst_1) : (tensor<f32>, tensor<2xi32>) -> tensor<1x1xf32>
  func.return %1 : tensor<1x1xf32>

// CHECK-LABEL: FoldReduceProdKeepDim

  let results = (outs
    TFL_TensorOf<[F32, I32, I64, QI8, QUI8, TFL_Quint8, QI16]>:$output);

  let hasOptions = 1;
  let customOption = "ReducerOptions";
}

def TFL_ReduceProdOp: TFL_Op<"reduce_prod", [
    PredOpTrait<"input and output must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>,
    QuantizableResult,
    Pure]> {
  let summary = "Prod-reduction operator";

    *   More consistent GcsFileSystem behavior for certain reads past EOF.
    *   Update benchmark for tf.scan to match ranges across eager and graph
        modes.
    *   Fixed bug in `tf.reduce_prod gradient` for complex dtypes.
    *   Allow the use of '.' in variables (e.g. "hparams.parse('a.b=1.0')"),
        which would previously raise an error. This will correspond to an

    "TILE",
    "EXPAND_DIMS",
    "EQUAL",
    "NOT_EQUAL",
    "LOG",
    "SUM",
    "SQRT",
    "RSQRT",
    "SHAPE",
    "POW",
    "ARG_MIN",
    "FAKE_QUANT",
    "REDUCE_PROD",
    "REDUCE_MAX",
    "PACK",
    "LOGICAL_OR",
    "ONE_HOT",
    "LOGICAL_AND",
    "LOGICAL_NOT",
    "UNPACK",
    "REDUCE_MIN",
    "FLOOR_DIV",
    "REDUCE_ANY",