%0 = "tf.Exp"(%arg0) : (tensor<?xf32>) -> tensor<?xf32>
  func.return %0 : tensor<?xf32>
}

// -----

// CHECK-LABEL: @floor
func.func @floor(%arg0: tensor<2xf32>) -> tensor<2xf32> {
  // CHECK:  mhlo.floor %arg0 : tensor<2xf32>
  %0 = "tf.Floor"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>
}

// -----

// CHECK-LABEL: func @floor_dynamic

}

// CHECK-LABEL:   func @floor(
// CHECK-SAME:                %[[VAL_0:.*]]: tensor<2xf32>) -> tensor<2xf32> {
// CHECK:           %[[VAL_1:.*]] = "tf.Floor"(%[[VAL_0]]) : (tensor<2xf32>) -> tensor<2xf32>
// CHECK:           return %[[VAL_1]] : tensor<2xf32>
// CHECK:         }
func.func @floor(%arg0: tensor<2xf32>) -> tensor<2xf32> {
  %0 = "mhlo.floor"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>

      // performance.

      // For Exponent = 2, the expected number of collisions per shuffle is
      // maximized at n = floor((2^32-1)^(1/2)) = 65535 where the expectation is
      // about 1/2.

      // For Exponent = 3, the expected number of collisions per shuffle is
      // maximized at n = floor((2^32-1)^(1/3)) = 1625 where the expectation is
      // about 1/3255.

		func(s *state, n *ir.CallExpr, args []*ssa.Value) *ssa.Value {
			return s.newValue1(ssa.OpCeil, types.Types[types.TFLOAT64], args[0])
		},
		sys.ARM64, sys.PPC64, sys.S390X, sys.Wasm)
	addF("math", "Floor",
		func(s *state, n *ir.CallExpr, args []*ssa.Value) *ssa.Value {
			return s.newValue1(ssa.OpFloor, types.Types[types.TFLOAT64], args[0])
		},
		sys.ARM64, sys.PPC64, sys.S390X, sys.Wasm)
	addF("math", "Round",

	v_0 := v.Args[0]
	// match: (FFLOOR (FMOVDconst [x]))
	// result: (FMOVDconst [math.Floor(x)])
	for {
		if v_0.Op != OpPPC64FMOVDconst {
			break
		}
		x := auxIntToFloat64(v_0.AuxInt)
		v.reset(OpPPC64FMOVDconst)
		v.AuxInt = float64ToAuxInt(math.Floor(x))
		return true
	}
	return false
}
func rewriteValuePPC64_OpPPC64FGreaterEqual(v *Value) bool {