%cst = "tf.Const"() <{value = dense<true> : tensor<i1>}> {device = ""} : () -> tensor<i1>
    %cst_0 = "tf.Const"() <{value = dense<[0.117216609, 0.933735609, 0.0728900209]> : tensor<3xf32>}> {device = ""} : () -> tensor<3xf32>

    %26 = "tf.Cast"(%25) {Truncate = false} : (tensor<2xi64>) -> tensor<2xf32>
    %27 = "tf.Equal"(%14, %26) {incompatible_shape_error = true} : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xi1>
    %28 = "tf.Cast"(%27) {Truncate = false} : (tensor<2xi1>) -> tensor<2xf32>
    %29 = "tf.Sum"(%28, %6) {keep_dims = false} : (tensor<2xf32>, tensor<1xi32>) -> tensor<f32>

  %0 = "tfr.cast"(%arg0) : (tensor<1x3x!quant.uniform<i8:f32:1, {0.1:1, 0.2:2, 0.3:3}>>) -> !tfr.tensor
  %scale, %zp = tfr.quant_qparam(%0) : (!tfr.tensor) -> (!tfr.tensor, !tfr.tensor)
  %1 = "tfr.cast"(%scale) : (!tfr.tensor) -> tensor<3xf32>
  %2 = "tfr.cast"(%zp) : (!tfr.tensor) -> tensor<3xi32>
  func.return %1, %2 : tensor<3xf32>, tensor<3xi32>

  // CHECK-SAME:      <[-1.000000e+00, 1.000000e+00]> : tensor<2xf32>}>
  // CHECK-SAME:      : (tensor<40x37xf32>) -> tensor<40x37xf32>
  // CHECK-NEXT:    %[[cst:.*]] = "tfl.pseudo_const"() <{value = dense<
  // CHECK-SAME:      1.000000e+00> : tensor<40xf32>}> : () -> tensor<40xf32>
  // CHECK-NEXT:    %[[fc:.*]]:2 = "tfl.fully_connected"(%[[stat]], %arg1,

  // CHECK: %[[VAL:.*]] = "tf.Const"() <{value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00]> : tensor<3xf32>}> : () -> tensor<3xf32>
  %value = "tf.Const"() {value = dense<[1.0, 2.0, 3.0]> : tensor<3xf32>} : () -> tensor<3xf32>
  // CHECK: %[[READ_VAR:.*]] = "tf.ReadVariableOp"(%[[VAR]])
  // CHECK: %[[UPDATE_SLICE:.*]] = "tf.Reshape"(%[[VAL]]
  // CHECK-SAME: -> tensor<1x3xf32>

  // expected-remark@above {{Sinks: {}}}
}

// -----

func.func @add(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  // expected-remark@above {{ID: 2}}
  %sum = "tf.Add"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  // expected-remark@above {{ID: 0}}
  func.return %sum : tensor<1xf32>
  // expected-remark@above {{ID: 1}}
  // expected-remark@above {{Sinks: {}}}
}

      "tfl.yield"(%wrap_result): (tensor<1xf32>)->()
      }): (tensor<1xf32>, tensor<1xf32>) -> (tensor<1xf32>)
  %tmp3 = "tfl.add"(%const, %tmp2) { fused_activation_function = "NONE" } : (tensor<1xf32>, tensor<1xf32>) -> (tensor<1xf32>)
  %tmp4 = "tf.AddV2"(%tmp2, %tmp2) { device = "" } : (tensor<1xf32>, tensor<1xf32>) -> (tensor<1xf32>)

  %perm = arith.constant dense<[3, 0, 1, 2]> : tensor<4xi32>
  %0 = "tfl.transpose"(%arg0, %perm) : (tensor<2x3x4x5xf32>, tensor<4xi32>) -> tensor<5x2x3x4xf32>
  %perm1 = arith.constant dense<[3, 0, 1, 2]> : tensor<4xi32>
  %1 = "tfl.transpose"(%arg1, %perm1) : (tensor<2x3x4x5xf32>, tensor<4xi32>) -> tensor<5x2x3x4xf32>
  %2 = tfl.add %0, %1 { fused_activation_function = "NONE" } : tensor<5x2x3x4xf32>

      %outputs_9, %control_10 = tf_executor.island wraps "tf.Mul"(%outputs_7, %outputs) {T = "tfdtype$DT_FLOAT", _tpu_replicate = "cluster", device = "", name = "mul"} : (tensor<0xf32>, tensor<f32>) -> tensor<0xf32>
      %outputs_11, %control_12 = tf_executor.island wraps "tf.Placeholder"() {device = "", dtype = "tfdtype$DT_FLOAT", name = "y", shape = "tfshape$dim { }"} : () -> tensor<0xf32>

  %cst_1 = "tf.Const"() {value = dense<[0.3, 0.4, 0.3, 0.4, 0.3, 0.4, 0.3, 0.4]> : tensor<8xf32>} : () -> tensor<8xf32>