func.func @cast_op_qint8_int8(%arg0: tensor<1x!tf_type.qint8>) -> tensor<1xi8> {
  // CHECK: "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<1xi8>) -> tensor<1xi8>
  %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x!tf_type.qint8>) -> tensor<1xi8>
  func.return %0: tensor<1xi8>
}

// -----

// CHECK-LABEL: func @cast_op_int8_qint8
func.func @cast_op_int8_qint8(%arg0: tensor<1xi8>) -> tensor<1x!tf_type.qint8> {

  %0 = "tfl.pseudo_sparse_qconst"() {compressed_data = dense<[1, 2, 3, 4, -1, -2, -3, -4]> : tensor<8xi8>, qtype = tensor<3x2x!quant.uniform<u8<1:255>:f32, 1.0>>, s_param = #tfl.sparsity_parameter<traversal_order = [0, 1, 2, 3], block_map...

  func.return %0 : tensor<i32>
}

// -----

// CHECK-LABEL: constant_tensor_vector
func.func @constant_tensor_vector(%arg0: vector<1x2xi32>) -> tensor<1x2xi32> {
  %0 = "tfr.constant_tensor"(%arg0) : (vector<1x2xi32>) -> tensor<1x2xi32>
  func.return %0 : tensor<1x2xi32>
}

// -----

// CHECK-LABEL: constant_tensor_array
func.func @constant_tensor_array() -> !tfr.tensor {

    %1 = "tf.Const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>
    %2 = "tf.Const"() {value = dense<-1> : tensor<i32>} : () -> tensor<i32>
    %3 = "tf.Const"() {value = dense<[[0, 1]]> : tensor<1x2xi32>} : () -> tensor<1x2xi32>
    %4 = "tf.Const"() {value = dense<> : tensor<0xi32>} : () -> tensor<0xi32>
    %5 = "tf.Const"() {value = dense<2.500000e-01> : tensor<f32>} : () -> tensor<f32>

    %cst_1 = "tfl.pseudo_qconst"() {qtype = tensor<1x20x!quant.uniform<i8:f32, 0.006:-34>>, value = dense<1> : tensor<1x20xi8>} : () -> tensor<1x20x!quant.uniform<i8:f32, 0.006:-34>>
    %cst_2 = "tfl.no_value"() {value = unit} : () -> none

// CHECK-SAME: tensor<1x3xf32>, tensor<1x3xf32>,
// CHECK-SAME: none, none, none, none)
// CHECK-SAME: -> tensor<1x2x3xf32>
// CHECK: "tfl.quantize"(%[[lstm]]) <{qtype = tensor<1x2x3x!quant.uniform<i16:f32, {{.*}}>>}> {volatile} : (tensor<1x2x3xf32>) -> tensor<1x2x3x!quant.uniform<i16:f32, {{.*}}>>

}

// CHECK-LABEL: QuantizeUnidirectionalLstmFullPerAxis

  %group_assignment = "tf.Const"() { value = dense<[[0, 1]]> : tensor<1x2xi32> } : () -> tensor<1x2xi32>
  %instance_key = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>
  %group_size, %group_key = "tf.CollectiveAssignGroupV2"(%group_assignment, %rank, %key_base) {} : (tensor<1x2xi32>, tensor<i32>, tensor<i32>) -> (tensor<i32>, tensor<i32>)
  // CHECK-NOT: "tf.CollectiveAssignGroupV2"

  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<1x2xi32>} : () -> tensor<1x2xi32>
  %1 = "tfl.pad"(%arg0, %0) : (tensor<15600xf32>, tensor<1x2xi32>) -> tensor<15600xf32>
  func.return %1 : tensor<15600xf32>
  // CHECK:  return %arg0
}

func.func @ConstFoldPadV2(%arg0: tensor<15600xf32>) -> tensor<15600xf32> {
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<1x2xi32>} : () -> tensor<1x2xi32>

        }) : () -> tensor<f32>
        "tf_device.cluster"() ({
          %group = "tf.Const"() <{value = dense<[[0, 1]]> : tensor<1x2xi32>}> : () -> tensor<1x2xi32>
          %arg1_reduced = "tf.XlaAllReduce"(%arg1_id, %group) <{mode = "CrossReplica", reduce_op = "Add"}> : (tensor<f32>, tensor<1x2xi32>) -> tensor<f32>
          "tf.OpA"(%arg1_reduced) : (tensor<f32>) -> ()
          tf_device.return
        }) : () -> ()

  // CHECK-SAME: %[[ARG2:.*]]: tensor<1x3xi1>
  // CHECK-SAME: %[[ARG3:.*]]: tensor<1x3xf32>
  func.func private @composite_and_select(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>, %arg2: tensor<1x3xi1>, %arg3: tensor<1x3xf32>) -> tensor<1x3xf32> {
    // CHECK: %[[Q1:.*]] = "quantfork.qcast"(%[[ARG0]]) {volatile} : (tensor<1x2xf32>) -> tensor<1x2x!quant.uniform<i8:f32, 5.000000e-03>>