%arg19: tensor<1x640xf32>,
  %arg20: tensor<1x2048xf32>
) -> tensor<1x640xf32> {
  %cst = "tfl.no_value"() {value = unit} : () -> none
  %zero = "tfl.pseudo_const"() {value = dense<0.0> : tensor<640xf32>} : () -> tensor<640xf32>
  %0 = "tfl.lstm"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5, %arg6, %arg7, %arg8, %cst, %cst, %cst, %arg9, %arg10, %arg11, %arg12, %arg13, %zero, %arg19, %arg20, %arg15, %arg16, %arg17, %arg18) ({}) {

//
// cst = value that is > 0
// rem = remainder(arg0, cst)
// for i in 0 to len(arg1):
//    if (rem[i] < 0 && rem[i] != 0)
//       rem[i] += cst
// return rem
def : Pat<(MHLO_SelectOp
            (MHLO_AndOp
              (MHLO_CompareOp:$rltz
                (MHLO_RemOp:$rem $arg, (MHLO_ConstantOp $cst)),
                (MHLO_ConstantOp $cst1),

    "tf.LookupTableImportV2"(%0, %cst, %cst_0) {_has_manual_control_dependencies = true, device = ""} : (tensor<!tf_type.resource>, tensor<3x!tf_type.string>, tensor<3xi64>) -> ()
    %1 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "hash_table_1", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>

    %1 = func.call @WhileOp_cond(%arg0, %arg1, %cst) : (tensor<*xi32>, tensor<*xf32>, tensor<i32>) -> tensor<i1>
    "tfl.yield"(%1) : (tensor<i1>) -> ()
  },  {
  ^bb0(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>):
    // CHECK: call @WhileOp_body
    // CHECK-SAME: (tensor<*xi32>, tensor<*xf32>, tensor<i32>)

  func.return %2 : tensor<2x2xf32>

// CHECK:  %[[cst:.*]] = "tfl.pseudo_const"(){{.*}}dense<-1.000000e-01> : tensor<2x2xf32>
// CHECK:  return %[[cst]] : tensor<2x2xf32>
}

// CHECK-LABEL: DequantizeAndQuantize
func.func @DequantizeAndQuantize() -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>> {

  // CHECK:  %cst = arith.constant dense<2> : tensor<1xi32>
  // CHECK:  %4 = "tfl.reduce_max"(%arg0, %cst) <{keep_dims = false}> : (tensor<4x32x256xf32>, tensor<1xi32>) -> tensor<4x32xf32>
  // CHECK:  %5 = "tfl.arg_max"(%arg0, %cst) : (tensor<4x32x256xf32>, tensor<1xi32>) -> tensor<4x32xi32>
  // CHECK:  return %4, %5 : tensor<4x32xf32>, tensor<4x32xi32>

}

// CHECK-LABEL: identity_with_const
func.func @identity_with_const() -> tensor<*xf32> {
  %cst = "tf.Const"() {device = "", value = dense<[2.167590e-01, 2.89403105]> : tensor<2xf32>} : () -> tensor<2xf32>
  %cst_1 = "tf.Const"() {device = "", value = dense<1.000000e-03> : tensor<f32>} : () -> tensor<f32>
  %0 = "tf.Identity"(%cst) {device = ""} : (tensor<2xf32>) -> tensor<*xf32>

    %cst = "tf.Const"() <{value = dense<[0, 1]> : tensor<2xi32>}> {device = ""} : () -> tensor<2xi32>
    %cst_0 = "tf.Const"() <{value = dense<1.000000e+01> : tensor<f32>}> {device = ""} : () -> tensor<f32>
    %0 = "tf.Sum"(%arg0, %cst) <{keep_dims = false}> {device = ""} : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<f32>

  func.return %1, %2 : tensor<2x1xf32>, tensor<2x1xf32>

// CHECK:  %cst = arith.constant
// CHECK:  %[[FQ:.*]] = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2)
// CHECK:  %[[T1:.*]] = "tf.Transpose"(%[[FQ]], %cst)
// CHECK:  %[[T2:.*]] = "tf.Transpose"(%[[FQ]], %cst)
}

// CHECK-LABEL: fakeQuantFollowedByReshape

  func.return %1, %2 : tensor<2x1xf32>, tensor<2x1xf32>

// CHECK:  %cst = arith.constant
// CHECK:  %[[FQ:.*]] = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2)
// CHECK:  %[[T1:.*]] = "tf.Transpose"(%[[FQ]], %cst)
// CHECK:  %[[T2:.*]] = "tf.Transpose"(%[[FQ]], %cst)
}

// CHECK-LABEL: fakeQuantFollowedByReshape