%index = arith.constant 0 : index
  %cst = arith.constant 1 : i32
  %eq = arith.cmpi eq, %n, %cst : i32
  %v1 = tfr.get_element %values[%index] : (!tfr.tensor_list, index) -> !tfr.tensor
  %temp = tfr.call @tf__expand_dims(%v1, %axis) : (!tfr.tensor, i32) -> !tfr.tensor
  %res = scf.if %eq -> !tfr.tensor {
    scf.yield %temp : !tfr.tensor
  } else {
    %step = arith.index_cast %cst : i32 to index

    %cst = "tf.Const"() {device = "", value = dense<[[[[-0.315365672, 0.27481091], [0.0901821703, -0.382271349], [-0.105572946, -0.354302853]], [[-0.47703138, -0.307006568], [0.306320101, -0.209111646], [0.252869487, 0.449634969]], [[0.167675957, 0.042408213],...

    %cst = "tf.Const"() {value = dense<1.0> : tensor<f32>} : () -> tensor<f32>
    %1 = "tf.Sub"(%arg3, %cst) : (tensor<f32>, tensor<f32>) -> tensor<f32>
    "tf.Yield"(%1, %arg4) : (tensor<f32>, tensor<*xf32>) -> ()
  }) {is_stateless = false} : (tensor<f32>, tensor<*xf32>) -> (tensor<f32>, tensor<*xf32>)
  func.return %0#0 : tensor<f32>

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string -

func.func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> {
  %cst = "tfl.pseudo_const"() {value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<?xi32>
  %0 = "tfl.add"(%arg0, %cst) {fused_activation_function = "NONE"} : (tensor<2xi32>, tensor<?xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}


// CHECK:    tensors: [ {
// CHECK-NEXT:      shape: [ 2 ],

  // CHECK-DAG: %[[CST:.+]] = mhlo.constant dense<[1.000000e-01, 2.000000e-01]> : tensor<2xf32>
  // CHECK-DAG: %[[CST_BCAST:.+]] = "mhlo.broadcast_in_dim"(%[[CST]]) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<2xf32>) -> tensor<1x1x3x2xf32>

  %1 = "tf.Add"(%0, %arg0): (tensor<i32>, tensor<*xi32>) -> tensor<*xi32>
  func.return %1: tensor<*xi32>
}

func.func @while_cond(%arg0: tensor<*xi32>) -> tensor<*xi1> {
  %cst = arith.constant dense<10> : tensor<i32>
  %0 = "tf.Less"(%arg0, %cst) {T = i32, device = ""} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi1>
  func.return %0 : tensor<*xi1>
}

    func.return
    // CHECK: %[[CST:.*]] = arith.constant dense<1.000000e+00> : tensor<1x10xf32>
    // CHECK: %[[RESOURCE:.*]] = "tfl.var_handle"() <{container = "c", shared_name = "a"}> : () -> tensor<!tf_type.resource<tensor<1x10xf32>>>
    // CHECK: "tfl.assign_variable"(%[[RESOURCE]], %[[CST]]) : (tensor<!tf_type.resource<tensor<1x10xf32>>>, tensor<1x10xf32>) -> ()
  }

// CHECK: func @main(%arg0: tensor<?x19x19x3xf32>) -> tensor<?x9x9x4xf32>
func.func @main(%arg0: tensor<?x19x19x3xf32>) -> tensor<?x9x9x4xf32> {
  %cst = arith.constant dense<1.0> : tensor<4xf32>
  %cst_3 = arith.constant dense<2.0> : tensor<4x3x3x3xf32>

  // CHECK: (%[[ARG0:.*]]: tensor<1x256x150x150xf32>, %[[ARG1:.*]]: tensor<256xf32>)
  // CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}>
  // CHECK: %[[R0:.*]] = "tf.Transpose"(%[[ARG0]], %[[CST]])
  // CHECK: %[[R1:.*]] = "tf.BiasAdd"(%[[R0]], %[[ARG1]]) <{data_format = "NHWC"}> {device = ""}

// -----

module {
  // CHECK-LABEL: conv_with_bias_and_relu
  func.func private @conv_with_bias_and_relu(%arg0: tensor<1x3x2x3xf32>) -> tensor<1x2x2x2xf32> {
    %cst = "tf.Const"() {device = "", value = dense<[7.11401462, 7.05456924]> : tensor<2xf32>} : () -> tensor<2xf32>
    // CHECK: %[[q_weight_per_channel:.*]] = "quantfork.qcast"