// QDQ-NEXT: return %[[q]] : tensor<384x128x!quant.uniform<i8:f32, 2.000000e+00>>
}

func.func @main(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x401408xf32> {
  %cst = arith.constant dense<[1, 401408]> : tensor<2xi32>
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>} : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>

  // CHECK-LABEL: RedundantShapeOp
  // CHECK-NOT: "tf.Shape"
}

// CHECK-LABEL: @MoveTransposeAcrossPerChannelQuant
func.func @MoveTransposeAcrossPerChannelQuant(%arg0 : tensor<1x224x224x3xf32>) -> tensor<1x112x112x6xf32> {
  %cst = "tf.Const"() <{value = dense<6.0> : tensor<6x3x7x7xf32>}> : () -> tensor<6x3x7x7xf32>
  %cst_14 = "tf.Const"() <{value = dense<[2, 3, 1, 0]> : tensor<4xi64>}> : () -> tensor<4xi64>

  %outputs_20 = "tf.ResizeBilinear"(%outputs_18, %outputs) {align_corners = false, device = "", half_pixel_centers = false} : (tensor<1x?x?x3xui8>, tensor<2xi32>) -> tensor<1x224x224x3xf32>
  %outputs_22 = "tf.Squeeze"(%outputs_20) {device = "", squeeze_dims = [0]} : (tensor<1x224x224x3xf32>) -> tensor<224x224x3xf32>
  %outputs_24 = "tf.Cast"(%outputs_22) {Truncate = false, device = ""} : (tensor<224x224x3xf32>) -> tensor<224x224x3xui8>

  func.return %2 : tensor<8x7x6x5x?x3x2x1xf32>
}

// -----

func.func @depthwise_conv_2D_invalid_bias(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x112x112x32xf32> {
  %w = arith.constant dense<127.0> : tensor<32x3x3x3xf32>
  %b = arith.constant dense<0.0> : tensor<32x1xf32>