%0 = stablehlo.uniform_dequantize %arg : (tensor<2x2x!quant.uniform<i8:f32, 1.000000e+0:8>>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}
// CHECK-LABEL: uniform_dequantize_op
// CHECK: %[[DEQUANT:.+]] = "tfl.dequantize"({{.*}}) : (tensor<2x2x!quant.uniform<i8:f32, 1.000000e+00:8>>) -> tensor<2x2xf32>
// CHECK: return %[[DEQUANT]]

// -----

/// (realValue: FloatAttr, quantizedElementType: UniformQuantizedType[i8:f32])
///   -> (IntegerAttr, outConvertedType: i8)
/// 2. realValue is an elements attribute:
/// (realValue: DenseElementsAttr[tensor<2x2xf32>],
///  quantizedElementType: UniformQuantizedType[i8:f32])
///   -> (DenseElementsAttr[tensor<2x2xi8>], outConvertedType: tensor<2x2xi8>)
Attribute quantizeAttr(Attribute realValue,

// CHECK-DAG: %[[input_19:.*]] = "quantfork.stats"({{.*}}) <{layerStats = dense<[-2.000000e+00, 4.000000e+00]> : tensor<2xf32>}> : (tensor<1x2xf32>) -> tensor<1x2xf32>

// CHECK: "tfl.unidirectional_sequence_lstm"({{.*}}, %[[input_18]], %[[input_19]], %{{[0-9]+}}, %{{[0-9]+}}, %{{[0-9]+}}, %{{[0-9]+}})

// RUN:   -split-input-file | FileCheck %s --check-prefix=STATIC-RANGE-PTQ-TO-COMPUTE-HEAVY

// STATIC-RANGE-PTQ-TO-COMPUTE-HEAVY: @main
func.func @main(%arg0: tensor<1x2xf32>) -> tensor<1x2xf32> {
  %0 = stablehlo.add %arg0, %arg0 : tensor<1x2xf32>
  return %0 : tensor<1x2xf32>
}
// Tests that `composite_add_fn_1` does not quantize when quantizing
// only compute-heavy ops.

    "tf_device.cluster"() ({
      %0 = "tf.RecvTPUEmbeddingActivations"() {config = "test_config_recv_embedding"} : () -> tensor<512x256xf32>
      "tf.SendTPUEmbeddingGradients"(%0) {N = 1 : i64, NN = 0 : i64, config = "test_config_send_embedding", operandSegmentSizes = array<i32: 1, 0>} : (tensor<512x256xf32>) -> ()
      "tf.A"() {_xla_outside_compilation = "cluster1"} : () -> ()
      tf_device.return

  func.func @tf_mod(%arg1: tensor<2x2xf32>) -> tensor<2x2xf32> {
    %cst = "tf.Const"() {value = dense<7.000000e+00> : tensor<f32>} : () -> tensor<f32>
    // CHECK: "mhlo.dynamic_broadcast_in_dim"
    // CHECK: mhlo.remainder
    %6 = "tf.Mod"(%arg1, %cst) {_global_shape = [#tf_type.shape<4x8>], device = ""} : (tensor<2x2xf32>, tensor<f32>) -> tensor<2x2xf32>
    return %6 : tensor<2x2xf32>
  }

  // CHECK-LABEL: func @concat_v2

// deferred.

// CHECK-LABEL: add_with_activation_transpose_rank_two
func.func @add_with_activation_transpose_rank_two(%arg0: tensor<1x2xf32>) -> tensor<2x1xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<2x1xf32>
  %1 = stablehlo.transpose %arg0, dims = [1, 0] : (tensor<1x2xf32>) -> tensor<2x1xf32>
  %2 = stablehlo.add %1, %0 : tensor<2x1xf32>
  return %2 : tensor<2x1xf32>
}

    "mhlo.return"(%10, %15) : (tensor<f32>, tensor<i32>) -> ()
  }) {dimensions = dense<2> : tensor<1xi64>} : (tensor<2x2x4xf32>, tensor<2x2x4xi32>, tensor<f32>, tensor<i32>) -> (tensor<2x2xf32>, tensor<2x2xi32>)
  func.return %4#0, %4#1 : tensor<2x2xf32>, tensor<2x2xi32>

  // CHECK-DAG:  %0 = mhlo.constant dense<0xFF800000> : tensor<f32>
  // CHECK-DAG:  %1 = mhlo.constant dense<0> : tensor<i32>

}

func.func @QDQsFollowedByTranspose(tensor<1x2xf32>) -> (tensor<2x1xf32>) {
^bb0(%arg0: tensor<1x2xf32>):
  %cst_0 = arith.constant dense<[1, 0]> : tensor<2xi32>
  %0 = "tfl.quantize"(%arg0){qtype = tensor<1x2x!quant.uniform<u8:f32, 1.0>>}: (tensor<1x2xf32>) -> (tensor<1x2x!quant.uniform<u8:f32, 1.0>>)
  %1 = "tfl.dequantize"(%0): (tensor<1x2x!quant.uniform<u8:f32, 1.0>>) -> (tensor<1x2xf32>)

# CHECK:  tf.TensorListReserve{{.*}}(tensor<2xi32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<*x!tf_type.variant>>>

# CHECK:  tf.TensorListSetItem{{.*}}(tensor<!tf_type.variant<tensor<*xf32>>>, tensor<i32>, tensor<2x2xf32>) -> tensor<*x!tf_type.variant>