func.func @add_with_activation_transpose_permutation_mismatch(
      %arg0: tensor<1x2x3x4xf32>) -> tensor<1x3x2x4xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<1x3x2x4xf32>
  %1 = stablehlo.transpose %arg0, dims = [0, 2, 1, 3] : (tensor<1x2x3x4xf32>) -> tensor<1x3x2x4xf32>
  %2 = stablehlo.add %1, %0 : tensor<1x3x2x4xf32>
  return %2 : tensor<1x3x2x4xf32>
}

  func.return %1 : tensor<1x2x2x5xf32>

// CHECK: %0 = "tfl.dequantize"(%arg0)
// CHECK: %1 = "tfl.strided_slice"(%0, %arg1, %arg2, %arg3)
// CHECK: %2 = "tfl.quantize"(%1) <{qtype = tensor<1x2x2x5x!quant.uniform<u8:f32, 1.000000e-01>>}> {volatile}
// CHECK: %3 = "tfl.dequantize"(%2)

func.func @partial_tile_partitioned_variable(%arg0: tensor<!tf_type.resource<tensor<1x4x4x4xf32>>>) {
  %0 = "tf.TPUPartitionedInputV2"(%arg0) {_XlaSharding = "\08\03\1A\05\04\01\01\01\02\22\08\00\01\02\03\04\05\06\070\01", partition_dims = [4, 1, 1, 1, 2], is_packed = true} : (tensor<!tf_type.resource<tensor<1x4x4x4xf32>>>) -> tensor<!tf_type.resource<tensor<4x4x4x4xf32>>>

func.func @FakeQuantWithMinMaxVarsPerChannel(tensor<1x2x3x8xf32>, tensor<8xf32>, tensor<8xf32>) -> tensor<1x2x3x8xf32> {
^bb0(%arg0: tensor<1x2x3x8xf32>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>):
  %0 = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg0, %arg1, %arg2) : (tensor<1x2x3x8xf32>, tensor<8xf32>, tensor<8xf32>) -> tensor<1x2x3x8xf32>
  func.return %0 : tensor<1x2x3x8xf32>
}

// -----

    // CHECK-NEXT: %[[CMP:.*]] = mhlo.compare GT, %[[INP]], %[[ZERO]], NOTYPE : (tensor<1x4x4x3xf32>, tensor<1x4x4x3xf32>) -> tensor<1x4x4x3xi1>
    // CHECK-NEXT: %[[RES:.*]] = mhlo.select %[[CMP]], %[[INP]], %[[LEAKY]] : tensor<1x4x4x3xi1>, tensor<1x4x4x3xf32>
    // CHECK-NEXT: return %[[RES]] : tensor<1x4x4x3xf32>
    %0 = "tf.LeakyRelu"(%arg0) {alpha = 2.000000e-01 : f32, device = ""} : (tensor<1x4x4x3xf32>) -> tensor<1x4x4x3xf32>

    quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
  } : (tensor<1x2x2x1x!tf_type.qint32>, tensor<f32>, tensor<i32>) -> tensor<1x2x2x1xf32>

  return %output : tensor<1x2x2x1xi32>