%0 = stablehlo.constant() {value = dense<3> : tensor<3x3x4x2xi8>} : () -> tensor<3x3x4x2x!quant.uniform<i8:f32, 3.000000e-01:-5>>
  %1 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x3x4xf32>, tensor<3x3x4x2x!quant.uniform<i8:f32, 3.000000e-01:-5>>) -> tensor<1x3x3x2xf32>

  %result = "tf_device.launch"() ({
    %3 = "tf.Transpose"(%2, %1) : (tensor<1x8x4x4xf32>, tensor<4xi32>) -> tensor<1x4x4x8xf32>
    tf_device.return %3: tensor<1x4x4x8xf32>
  }) {device = "device"} : () -> tensor<1x4x4x8xf32>

  func.return %result : tensor<1x4x4x8xf32>

  // CHECK-DAG: %[[CONST1:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}>

// CHECK-LABEL: func @testValidFakeQuantWithMinMaxArgs
func.func @testValidFakeQuantWithMinMaxArgs(tensor<8x8x8x8xf32>) -> tensor<8x8x8x8xf32> {
^bb0(%arg0: tensor<8x8x8x8xf32>):
  %0 = "tf.FakeQuantWithMinMaxArgs"(%arg0) {min = -1.0 : f32, max = 1.0 : f32, num_bits = 3} : (tensor<8x8x8x8xf32>) -> tensor<8x8x8x8xf32>
  func.return %0 : tensor<8x8x8x8xf32>
}

// -----

// Test invalid tf.FakeQuantWithMinMaxArgs

}

// -----

func.func @transpose_uniform_qtype(%arg0 : tensor<1x3x4x3xf32>) -> tensor<3x4x3x1x!quant.uniform<i8:f32, 0.0078356266021728515:-1>> {
  %cst = arith.constant dense<[1, 2, 3, 0]> : tensor<4xi32>
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0078356266021728515:-1>>} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0078356266021728515:-1>>
  // CHECK: "tfl.transpose"