%dequant = stablehlo.uniform_dequantize %requant : (tensor<1x3x4x2x!quant.uniform<i8:f32, 0.045673100153605144:-62>>) -> tensor<1x3x4x2xf32>
  func.return %dequant : tensor<1x3x4x2xf32>
}

// -----

// CHECK-LABEL: @dont_merge_quantization_followed_by_quantization
// CHECK-SAME: %[[ARG_0:.*]]: tensor<1x3x4x3xf32>

    %2 = "quantfork.stats"(%1) {layerStats = dense<[3.50919247, 6.000000e+00]> : tensor<2xf32>} : (tensor<1x3x4x2xf32>) -> tensor<1x3x4x2xf32>
    %3 = "tf.Identity"(%2) {device = ""} : (tensor<1x3x4x2xf32>) -> tensor<1x3x4x2xf32>
    %4 = "quantfork.stats"(%3) {layerStats = dense<[3.50919247, 6.000000e+00]> : tensor<2xf32>} : (tensor<1x3x4x2xf32>) -> tensor<1x3x4x2xf32>
    func.return %4 : tensor<1x3x4x2xf32>

      } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>, tensor<2xf32>) -> 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_1d_fn(%[[ARG_0:.+]]: tensor<1x3x4x3xf32>) -> tensor<1x3x4x2xf32> attributes {tf._original_func_name = "main_0"}

        _stablehlo_module_attrs = {}, _tfl_quant_trait = "fully_quantizable",
        device = ""
      } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32>
    return %0 : tensor<1x3x4x2xf32>
  }

  // CHECK: func.func private @qdq_for_conv_weight_per_channel_default(%[[ARG0:.+]]: tensor<1x3x4x3xf32>)

// CHECK:  %1 = "tfl.depthwise_conv_2d"(%arg0, %0, %[[CONSTANT]]) <{depth_multiplier = 4 : i32, dilation_h_factor = 2 : i32, dilation_w_factor = 3 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 4 : i32, stride_w = 5 : i32}> : (tensor<256x32x32x3xf32>, tensor<1x3x3x12xf32>, tensor<12xf32>) -> tensor<256x30x30x12xf32>

  func.return %0 : tensor<?x?x?x?x?xf32>
}

// -----

func.func @testConv3dInvalidBiasShape(%arg0: tensor<2x3x4x5x2xf32>,%arg1:  tensor<2x2x2x2x3xf32>,%arg2: tensor<4xf32>) -> tensor<?x?x?x?x?xf32> {