%0:2 = "tf.TopKV2"(%arg0, %arg1): (tensor<16xf32>, tensor<i32>) -> (tensor<?xf32>, tensor<?xi32>)
    func.return %0#0, %0#1: tensor<?xf32>, tensor<?xi32>
  }

// CHECK: func.func private @topk(%arg0: tensor<16xf32>, %arg1: tensor<i32>) -> (tensor<?xf32>, tensor<?xi32>)

    %cst = arith.constant dense<[2, 100]> : tensor<2xi64>
    %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<200xf32>
    %1 = "tfl.reshape"(%0, %cst) : (tensor<200xf32>, tensor<2xi64>) -> tensor<2x100xf32>
    func.return %1 : tensor<2x100xf32>
  }

          tensor<f32>, tensor<i32>) -> tensor<2x!tf_type.qint32>
    func.return %1 : tensor<2x!tf_type.qint32>
}

// -----

// CHECK-LABEL: func @while_region_qint
func.func @while_region_qint(%arg0: tensor<2x2xf32>) -> (tensor<2x?xf32>, tensor<?x2xf32>) {
  %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
  %zps2 = "tf.Const"() { value = dense<2> : tensor<i32> } : () -> tensor<i32>

	case size == -1:
		if size := data.ActualSize(); size > 0 && size < fi.Erasure.BlockSize {
			// Account for padding and forced compression overhead and encryption.
			buffer = make([]byte, data.ActualSize()+256+32+32, data.ActualSize()*2+512)
		} else {
			buffer = globalBytePoolCap.Load().Get()
			defer globalBytePoolCap.Load().Put(buffer)
		}
	case size >= fi.Erasure.BlockSize:
		buffer = globalBytePoolCap.Load().Get()