%4 = mhlo.reshape %3 : (tensor<1x8x8x1xf32>) -> tensor<8x8xf32>
  %5 = "mhlo.broadcast_in_dim"(%4) <{broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>}> : (tensor<8x8xf32>) -> tensor<4x8x8x8xf32>
  %6 = mhlo.divide %2, %5 : tensor<4x8x8x8xf32>
  return %6 : tensor<4x8x8x8xf32>
}

// CHECK-LABEL:   func @convert_maxpool_valid(

}

func.func @fakeQuantArgsTrue(%arg0: tensor<8x8x8x8xf32>) -> tensor<8x8x8x8xf32> {
  %0 = "tf.FakeQuantWithMinMaxArgs"(%arg0) {min = -0.1 : f32, max = 0.2 : f32, num_bits = 5, narrow_range = true} : (tensor<8x8x8x8xf32>) -> tensor<8x8x8x8xf32>
  func.return %0 : tensor<8x8x8x8xf32>

  // CHECK-LABEL: fakeQuantArgsTrue

  func.return %0 : tensor<256x16x32x32xf32>

  // LAYOUT-LABEL: Conv2dNCHW
  // LAYOUT: "tfl.conv_2d"
}

func.func @fusedBatchNormV3(tensor<8x8x8x8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>) -> (tensor<8x8x8x8xf32>, tensor<8xf32>) {
^bb0(%arg0: tensor<8x8x8x8xf32>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>, %arg3: tensor<8xf32>, %arg4: tensor<8xf32>):
  // OK

// CHECK: %[[RESULT:.*]] = "tfl.reshape"(%arg0, %[[CONST:.*]]) : (tensor<?x1x8x3xf32>, tensor<3xi32>) -> tensor<?x8x3xf32>
// CHECK:  return %[[RESULT]]
}

func.func @ConvertSqueezeToReshapeWithDynamicDimension2(%arg0: tensor<?x1x8x3xf32>) -> tensor<1x8x3xf32> {
  %0 = "tfl.squeeze"(%arg0) {squeeze_dims = [0]}: (tensor<?x1x8x3xf32>) -> tensor<1x8x3xf32>
  func.return %0: tensor<1x8x3xf32>