%res1 = "tf.MulNoNan"(%arg0, %comp1) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<2xcomplex<f32>>, tensor<2xcomplex<f32>>) -> tensor<2xcomplex<f32>>
  %res2 = "tf.DivNoNan"(%arg0, %comp2) : (tensor<2xcomplex<f32>>, tensor<2xcomplex<f32>>) -> tensor<2xcomplex<f32>>
  %res3 = "tf.MulNoNan"(%arg0, %comp3) : (tensor<2xcomplex<f32>>, tensor<2xcomplex<f32>>) -> tensor<2xcomplex<f32>>

/// Canonicalization template for tf.DivNoNan and tf.MulNoNan:
/// If the op is tf.DivNoNan and the divisor is a constant tensor (with all the
/// elements of any allowed type: float or complex), rewrite the op to the
/// divisor if all the elements of the divisor are zero and to tf.Div if all the
/// elements of the divisor are non-zero.

/// Similarly, if the op is tf.MulNoNan and the multiplier is a constant tensor

            "Lgamma", "Digamma",
            // Binary
            "Add", "AddV2", "Sub", "Mul", "Div", "Atan2", "Complex", "DivNoNan",
            "MulNoNan", "FloorDiv", "Xlogy", "Xlog1py", "Xdivy", "FloorMod",
            "BitwiseAnd", "BitwiseOr", "BitwiseXor", "LeftShift", "RightShift",
            "LogicalAnd", "LogicalOr", "Mod", "Maximum", "Minimum", "RealDiv",

  // CHECK:  %[[RESULT:.*]] = "tf.SelectV2"(%[[IS_ZERO]], %[[ZERO]], %[[MUL]]) : (tensor<3xi1>, tensor<f32>, tensor<2x3xf32>) -> tensor<2x3xf32>
  %0 = "tf.MulNoNan"(%arg0, %arg1) : (tensor<2x3xf32>, tensor<3xf32>) -> tensor<2x3xf32>

  // CHECK: return %[[RESULT]]
  func.return %0 : tensor<2x3xf32>
}

// CHECK-LABEL: @is_inf

  let hasFolder = 1;
}

def TF_MulNoNanOp : TF_Op<"MulNoNan", [Pure, ResultsBroadcastableShape]>,
                    WithBroadcastableBinOpBuilder {
  let summary = [{
Returns x * y element-wise. Returns zero if y is zero, even if x if infinite or NaN.
  }];

  let description = [{
*NOTE*: `MulNoNan` supports broadcasting. More about broadcasting

    *   Unary GPU kernels: Abs, Atanh, Acos, Acosh, Asin, Asinh, Atan, Cos, Cosh, Sin, Sinh, Tan, Tanh.
    *   Binary GPU kernels: AddV2, Sub, Div, DivNoNan, Mul, MulNoNan, FloorDiv, Equal, NotEqual, Greater, GreaterEqual, LessEqual, Less.

* `tf.lite`
    * Add experimental supports conversion of models that may be larger than 2GB before buffer deduplication