// CHECK: return %[[RESULT]]

  %indices = "tf.Const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
  %0 = "tf.DynamicStitch"(%indices, %arg0) : (tensor<2xi32>, tensor<2x2x2xf32>) -> tensor<2x2x2xf32>
  func.return %0 : tensor<2x2x2xf32>
}

// CHECK-LABEL: func @DynamicStitch_dynamic

    return %2 : tensor<1x2xf32>
  }

  func.func private @composite_add_fn(%arg0: tensor<1x2xf32>, %arg1: tensor<1x2xf32>) -> tensor<1x2xf32> attributes {_from_xla_call_module} {
    %0 = stablehlo.add %arg0, %arg1 : tensor<1x2xf32>
    %1 = stablehlo.add %0, %arg1 : tensor<1x2xf32>
    return %1 : tensor<1x2xf32>
  }
}

// -----

    %0 = "tf.Div"(%arg1, %cst_0) : (tensor<2x2x2xf32>, tensor<f32>) -> tensor<2x2x2xf32>
    %1 = "tf.AddV2"(%0, %cst_1) : (tensor<2x2x2xf32>, tensor<f32>) -> tensor<2x2x2xf32>
    %2 = "tf.Floor"(%1) : (tensor<2x2x2xf32>) -> tensor<2x2x2xf32>
    %3 = "tf.ClipByValue"(%2, %cst_5, %cst_6) : (tensor<2x2x2xf32>, tensor<f32>, tensor<f32>) -> tensor<2x2x2xf32>
    %4 = "tf.Cast"(%3) {Truncate = false} : (tensor<2x2x2xf32>) -> tensor<2x2x2xi8>

    %0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_b", device = "", transpose_a = false, transpose_b = false} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
    return %0 : tensor<2x2xf32>
  }

// Legacy mode re-calculates zero point when it's changed due to subtle difference in scale.
func.func @ZeroPointLegacy(%arg0: tensor<1x2xf32>) -> tensor<1x2xf32>  {
  %0 = "quantfork.stats"(%arg0) {layerStats = dense<[-1.0, 1.20779215]> : tensor<2xf32>} : (tensor<1x2xf32>) -> tensor<1x2xf32>
  func.return %0 : tensor<1x2xf32>
// CHECK: %1 = "tfl.dequantize"(%0) : (tensor<1x2x!quant.uniform<i8:f32, 0.0086580084819419707:-12>>)

}

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>)

//   %arg1)
//     : (tensor<i32>, tensor<2x2xi32>, tensor<2xf32>, tensor<2x2x2xf32>)
//     -> tensor<5x2xf32>
//
// is lowered to
//
//   %shape = "tf.Const"() {value = dense<[-1, 2]> : tensor<2xi64>}
//   %inp0 = "tf.Reshape"(%arg0, %shape)
//     : (tensor<2xf32>, tensor<2xi64>) -> tensor<1x2xf32>
//   %inp1 = "tf.Reshape"(%arg1, %shape)
//     : (tensor<2x2x2xf32>, tensor<2xi64>) -> tensor<4x2xf32>

// CHECK-SAME:                    %[[ARG_0:.*]]: tensor<1x2xf32>
func.func @dot_general_with_bias_same_shape_fn(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<2x3xf32>
  %1 = stablehlo.constant dense<2.000000e+00> : tensor<1x3xf32>
  %2 = stablehlo.dot_general %arg0, %0, contracting_dims = [1] x [0], precision = [DEFAULT, DEFAULT] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>