"tfl.custom"(%0, %2) {custom_code = "FlexAssignVariableOp", custom_option = #tfl<const_bytes : "0x1041737369676E5661726961626C654F70003B121041737369676E5661726961626C654F701A001A002A0B0A056474797065120230012A140A0E76616C69646174655F736861706512022800320000024F3E1414042801">} : (tensor<!tf_type.resource<tensor<1x10xf32>>>, tensor<1x10xf32>) -> ()

    %recurrent_stats = "quantfork.stats"(%recurrent_input) {layerStats = dense<[-2.0, 1.0]> : tensor<2xf32>} : (tensor<1x20xf32>) -> tensor<1x20xf32>
    %cell_input = arith.constant dense<1.0> : tensor<1x20xf32>
    %cell_stats = "quantfork.stats"(%cell_input) {layerStats = dense<[-2.73090601, 7.94872093]> : tensor<2xf32>} : (tensor<1x20xf32>) -> tensor<1x20xf32>
    %0 = "tfl.unidirectional_sequence_lstm"(%arg0,

// CHECK-DAG:       [[VAL_20:%.*]] = "tf.Const"() <{value = dense<-1> : tensor<1xi32>}> : () -> tensor<1xi32>
// CHECK:           [[VAL_21:%.*]] = "tf.Reshape"([[VAL_15]]#0, [[VAL_20]]) : (tensor<1x10xf32>, tensor<1xi32>) -> tensor<10xf32>

    %0 = "tfl.batch_matmul"(%arg0, %cst_0) {adj_x = false, adj_y = false} : (tensor<1x256xf32>, tensor<256x256xi8>) -> tensor<1x256xf32>
    %1 = "tfl.batch_matmul"(%0, %cst_1) {adj_x = false, adj_y = false} : (tensor<1x256xf32>, tensor<256x256x!quant.uniform<i8:f32, 1.000000e+00>>) -> tensor<1x256xf32>
    %2:2 = "tfl.while"(%cst_2, %1) ({
    ^bb0(%arg1: tensor<i32>,  %arg2: tensor<1x256xf32>):
      %cst_3 = arith.constant dense<10> : tensor<i32>

  // CHECK: %[[ADD:.*]] = tfl.add %[[VAR_VAL]], %arg0 {fused_activation_function = "NONE"} : tensor<1x10xf32>
  // CHECK: "tfl.assign_variable"(%[[RESOURCE]], %[[ADD]]) : (tensor<!tf_type.resource<tensor<1x10xf32>>>, tensor<1x10xf32>) -> ()
  // CHECK: %[[RESULT:.*]] = "tfl.read_variable"(%[[RESOURCE]]) : (tensor<!tf_type.resource<tensor<1x10xf32>>>) -> tensor<1x10xf32>

  %0:4 = "tfl.basic_lstm"(%arg0, %arg1, %arg2, %arg3, %arg4) {fused_activation_function = "RELU", cell_clip = 1.0 : f32, proj_clip = 2.0 : f32} : (tensor<1x384xf32>, tensor<1x96xf32>, tensor<384x480xf32>, tensor<384xf32>, tensor<1x96xf32>) -> (tensor<1x96xf32>, tensor<1x96xf32>, tensor<1x480xf32>, tensor<1x384xf32>)
  func.return %0#0 : tensor<1x96xf32>

// -----

// CHECK-LABEL: float_matmul
func.func @float_matmul(
  %arg0: tensor<1x10xf32>, %arg1: tensor<10x10xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
  %0 = "tf.MatMul"(%arg0, %arg1) {
    transpose_a = false, transpose_b = false
  } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<*xf32>

    %recurrent_stats = "quantfork.stats"(%recurrent_input) {layerStats = dense<[-2.0, 1.0]> : tensor<2xf32>} : (tensor<1x20xf32>) -> tensor<1x20xf32>
    %cell_input = arith.constant dense<1.0> : tensor<1x20xf32>
    %cell_stats = "quantfork.stats"(%cell_input) {layerStats = dense<[-2.73090601, 7.94872093]> : tensor<2xf32>} : (tensor<1x20xf32>) -> tensor<1x20xf32>

    }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<1x256xf32>, tensor<f32>) -> tensor<1xf32>
    %5  = mhlo.add %3, %arg6 : tensor<1xf32>
    "mhlo.return"(%1, %arg3, %arg4, %arg5, %5) : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<1x256xf32>, tensor<1xf32>) -> ()
  }) : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<1x256xf32>, tensor<1xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<1x256xf32>, tensor<1xf32>)

    %1196 = "tfl.reshape"(%arg0, %arg1) : (tensor<?x10x1xf32>, tensor<6xi32>) -> tensor<1x?x1x10x1x1xf32>
    %1197 = "tfl.broadcast_to"(%1196, %arg2) : (tensor<1x?x1x10x1x1xf32>, tensor<6xi32>) -> tensor<1x?x1x10x5x1xf32>
    %1198 = "tfl.reshape"(%1197, %arg3) : (tensor<1x?x1x10x5x1xf32>, tensor<2xi32>) -> tensor<?x50xf32>
    return %1198 : tensor<?x50xf32>