%partitioned_out:2, %partitioned_control = tf_executor.island wraps "tf.TPUPartitionedOutput"(%identity_out) {partition_dim = 0 : i64} : (tensor<4x4xf32>) -> (tensor<2x4xf32>, tensor<2x4xf32>)
    %identity_out1, %control_identity1 = tf_executor.island wraps "tf.Identity"(%partitioned_out#0) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32>

  %1 = "tfl.quantize"(%0) {qtype = tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>>} : (tensor<2x1xf32>) -> tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>>
  %2 = "tfl.dequantize"(%1) : (tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>>) -> tensor<2x1xf32>

  }

  // CHECK-LABEL: pack
  func.func @pack(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<2x1xf32> {
    %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>
    func.return %0 : tensor<2x1xf32>
    // CHECK: %[[VAL_0:.*]] = arith.constant dense<[2, 1]> : tensor<2xi32>

    %result = "tf.IfrtCall"(%arg0, %2) <{program_id = 6515870160938153680 : i64, variable_arg_indices = []}> : (tensor<1x3xf32>, tensor<3x1xf32>) -> (tensor<1x1xf32>)
    return %result, %3 : tensor<1x1xf32>, tensor<1x1xf32>
  }
}

// -----
// Variable tensor is only for host
//
// CHECK-LABEL:  func.func @serving_default(%arg0: tensor<1x3xf32>) -> tensor<1x1xf32> {
// CHECK:  "tf.VarHandleOp"

  %1 = "tf.ReadVariableOp"(%0) {device = "/device:CPU:0"} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>
  %2 = "tf.AddV2"(%arg0, %1) {device = "/device:CPU:0"} : (tensor<1x3xf32>, tensor<1x3xf32>) -> tensor<1x3xf32>
  %3 = "tf.Identity"(%2) {device = "/device:CPU:0"} : (tensor<1x3xf32>) -> tensor<1x3xf32>
  func.return %3 : tensor<1x3xf32>
}

// CHECK-LABEL: func @main

// Confirm we can extract type info from reshape

func.func @main() -> tensor<2x2xf32> {
  // CHECK: %[[cst:.*]] = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32>
  // CHECK: %{{.*}} = "tfl.reshape"(%{{.*}}, %[[cst]]) : (tensor<4xf32>, tensor<2xi32>) -> tensor<2x2xf32>
  %cst = arith.constant dense<[2, 2]> : tensor<2xi32>

  %1 = "tf.Transpose"(%0, %cst_0): (tensor<1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>
  %2 = "tf.Transpose"(%0, %cst_0): (tensor<1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>
  func.return %1, %2 : tensor<2x1xf32>, tensor<2x1xf32>

// CHECK:  %cst = arith.constant

  // CHECK: return [[RI]]
  func.return %ri : tensor<!tf_type.resource<tensor<10x3xf32>>>
}

// -----

// CHECK-LABEL:func @partitioned_2d
// CHECK-SAME: ([[ARG0:%.*]]: tensor<10x3xf32>, [[ARG1:%.*]]: tensor<10x3xf32>)
func.func @partitioned_2d(%arg0: tensor<10x3xf32>, %arg1: tensor<10x3xf32>) -> tensor<20x3xf32> {
  // CHECK: [[PI_0:%.*]] = "tf.TPUPartitionedInputV2"([[ARG0]], [[ARG1]])

  %2 = "tf.Const"() {value = dense<0.0> : tensor<2x2xf32>} : () -> tensor<2x2xf32>
  %3 = "tf.Const"() {value = dense<0.0> : tensor<4x4xf32>} : () -> tensor<4x4xf32>
  "tf.SendTPUEmbeddingGradients"(%2, %3) {_tpu_embedding_layer = "call1", config = "\0A\0B\0C\0D", operandSegmentSizes = array<i32: 2, 0>} : (tensor<2x2xf32>, tensor<4x4xf32>) -> ()

  %1 = "tf.Transpose"(%0, %cst_0): (tensor<1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>
  %2 = "tf.Transpose"(%0, %cst_0): (tensor<1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>
  func.return %1, %2 : tensor<2x1xf32>, tensor<2x1xf32>

// CHECK:  %cst = arith.constant