%0 = "tf.Cast"(%arg0) : (tensor<1x3x2x2xi8>) -> tensor<1x3x2x2xi32>
    %1 = "tf.Sub"(%0, %arg2) : (tensor<1x3x2x2xi32>, tensor<i32>) -> tensor<1x3x2x2xi32>
    %2 = "tf.Cast"(%1) : (tensor<1x3x2x2xi32>) -> tensor<1x3x2x2xf32>
    %3 = "tf.Mul"(%2, %arg1) : (tensor<1x3x2x2xf32>, tensor<f32>) -> tensor<1x3x2x2xf32>
    return %3 : tensor<1x3x2x2xf32>
  }

      } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>, tensor<1x1x1x2xf32>) -> tensor<1x3x4x2xf32>
    %2 = "quantfork.stats"(%1) {layerStats = dense<[5.00000000e-6, 7.00000000e-1]> : tensor<2xf32>} : (tensor<1x3x4x2xf32>) -> tensor<1x3x4x2xf32>
    return %2 : tensor<1x3x4x2xf32>
  }
// CHECK: func.func private @quantize_conv_with_bias_fn(%[[ARG_0:.+]]: tensor<1x3x4x3xf32>) -> tensor<1x3x4x2xf32> attributes {tf._original_func_name = "main_0"}

    // CHECK-NEXT:  %[[SELU_VAL:.*]] = "tf.Mul"(%[[ELU_VAL]], %[[SCALED_ALPHA]]) : (tensor<1x4x4x3xf32>, tensor<f32>) -> tensor<1x4x4x3xf32>
    // CHECK-NEXT:  %[[RES:.*]] = "tf.SelectV2"(%[[PRED]], %[[SCALED_FEATURES]], %[[SELU_VAL]]) : (tensor<1x4x4x3xi1>, tensor<1x4x4x3xf32>, tensor<1x4x4x3xf32>) -> tensor<1x4x4x3xf32>
    // CHECK-NEXT:  return %[[RES]] : tensor<1x4x4x3xf32>

  // CHECK:  %[[MUL3:.*]] = "tf.Mul"(%[[MEAN]], %[[MUL1]]) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  // CHECK:  %[[SUB:.*]] = "tf.Sub"(%arg2, %[[MUL3]]) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  // CHECK:  %[[ADD0:.*]] = "tf.Add"(%[[MUL2]], %[[SUB]]) : (tensor<1x1x6x2xf32>, tensor<2xf32>) -> tensor<1x1x6x2xf32>

  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)
// CHECK: return %3 : tensor<1x2x2x5xf32>
}

// CHECK-LABEL: QuantizePad