def : Pat<(TF_FloorDivOp AnyTensor:$l, AnyTensor:$r),
          (CHLO_BroadcastDivOp $l, $r, (BinBroadcastDimensions $l, $r)),
          [(UnsignedIntTensor $l)]>;

// Performs a substitution of FloorMod designed to correct for possibly negative
// values. Pseudocode shown below:
//
//   T trunc_mod = std::fmod(x, y);
//   return trunc_mod != 0 && (y < 0 != trunc_mod < 0) ? trunc_mod + y

  // CHECK-NEXT: return [[SELECT]]
  %0 = "tf.FloorMod"(%arg0, %arg1) : (tensor<?x?xi32>, tensor<?xi32>) -> tensor<?x?xi32>
  func.return %0: tensor<?x?xi32>
}

// -----

// CHECK-LABEL: func @floormod_dynamic_broadcast_denominator
func.func @floormod_dynamic_broadcast_denominator_(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
  // CHECK-NOT: tf.FloorMod

    "ONE_HOT",
    "LOGICAL_AND",
    "LOGICAL_NOT",
    "UNPACK",
    "REDUCE_MIN",
    "FLOOR_DIV",
    "REDUCE_ANY",
    "SQUARE",
    "ZEROS_LIKE",
    "FILL",
    "FLOOR_MOD",
    "RANGE",
    "RESIZE_NEAREST_NEIGHBOR",
    "LEAKY_RELU",
    "SQUARED_DIFFERENCE",
    "MIRROR_PAD",
    "ABS",
    "SPLIT_V",
    "UNIQUE",
    "CEIL",
    "REVERSE_V2",

  func.return %15 : tensor<8x128xbf16>
}

// CHECK-LABEL: func @convert_floor_mod_float
// CHECK: %[[RESULT:.*]] = "tf.FloorMod"(%arg0, %arg1) : (tensor<192x8xbf16>, tensor<192x8xbf16>) -> tensor<192x8xbf16>
// CHECK: return %[[RESULT]]
// CHECK: }

    *   Added 16-bit int type support for built-in op `less`, `greater_than`, `equal`
    *   Added 8-bit and 16-bit support for `floor_div` and `floor_mod`.
    *   Added 16-bit and 32-bit int support for the built-in op `bitcast`.
    *   Added 8-bit/16-bit/32-bit int/uint support for the built-in op `bitwise_xor`

            "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",

  %6 = "tf.Const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
  %7 = "tf.FloorDiv"(%arg1, %5) {device = ""} : (tensor<1x1x2x1xi32>, tensor<i32>) -> tensor<1x1x2x1xi32>
  %8 = "tf.FloorMod"(%7, %4) {device = ""} : (tensor<1x1x2x1xi32>, tensor<i32>) -> tensor<1x1x2x1xi32>
  %9 = "tf.FloorDiv"(%arg1, %4) {device = ""} : (tensor<1x1x2x1xi32>, tensor<i32>) -> tensor<1x1x2x1xi32>

        op.getLoc(),
        ChangeTensorElementType(&rewriter, out_type, rewriter.getF32Type()), l,
        r);

    auto floor_op =
        rewriter.create<ConvertOp>(op.getLoc(), out_type, intermediate);
    rewriter.replaceOp(op, floor_op.getResult());
    return success();
  }
};

class ConvertBroadcastToOp : public OpRewritePattern<TF::BroadcastToOp> {
 public:

}

def TF_FloorModOp : TF_Op<"FloorMod", [Pure, ResultsBroadcastableShape, TF_SameOperandsAndResultElementTypeResolveRef]>,
                    WithBroadcastableBinOpBuilder {
  let summary = "Returns element-wise remainder of division.";

  let description = [{
This follows Python semantics in that the
result here is consistent with a flooring divide. E.g.
`floor(x / y) * y + floormod(x, y) = x`, regardless of the signs of x and y.