%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(

// permutated to match the new input shape.
// CHECK: (tensor<1x16x16x4xf32>, tensor<f32>) -> tensor<1x8x8x4xf32>

// Check that a `stablehlo.transpose` is added to the result to match the shape
// of the users.
// CHECK: %[[TRANSPOSE:.+]] = stablehlo.transpose %[[REDUCE_WINDOW]], dims = [0, 3, 1, 2] : (tensor<1x8x8x4xf32>) -> tensor<1x4x8x8xf32>
// CHECK: return %[[TRANSPOSE]]

// -----

  func.return %0#0, %1#0 : tensor<8x8x8x8xf32>, tensor<8x8x8x8xf32>
}
// CHECK: func @customAggregator
// CHECK-NEXT: %[[stats:.*]] = "quantfork.stats"(%arg0) <{layerStats = dense<[-1.000000e-01, 2.000000e-01]> : tensor<2xf32>}> : (tensor<8x8x8x8xf32>) -> tensor<8x8x8x8xf32>
// CHECK-NEXT: return %[[stats]], %arg0

  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

    func.return %0#0 : tensor<8x8x8x8xf32>
  }

  // CHECK-LABEL: fusedBatchNormGradV3_Training

  func.return %0#0 : tensor<8x8x8x8xf32>
}

// -----

// CHECK-LABEL: fusedBatchNormGrad_Training

}

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

// Contains the `stablehlo.transpose` op of the arg (e.g. [b, f, 0, 1] to
// [b, 0, 1, f]). The weight constant is folded into [0, 1, i, o] format.
// CHECK-DAG: %[[CST:.+]] = stablehlo.constant dense<3.000000e+00> : tensor<3x3x8x8xf32>
// CHECK: %[[TRANSPOSE_1:.+]] = stablehlo.transpose %arg0, dims = [0, 2, 3, 1] : (tensor<1x8x4x4xf32>) -> tensor<1x4x4x8xf32>

// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[TRANSPOSE_0]], %[[TRANSPOSE_1]]) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = {{\[\[}}1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x4x4x8xf32>, tensor<3x3x8x8xf32>) -> tensor<1x4x4x8xf32>

// CHECK:           [[VAL_40:%.*]] = arith.constant dense<0> : tensor<1xi32>
// CHECK:           [[VAL_41:%.*]] = "tf.ReverseV2"([[VAL_0]], [[VAL_40]]) : (tensor<8x8x8xf32>, tensor<1xi32>) -> tensor<8x8x8xf32>
// CHECK:           [[VAL_6:%.*]] = arith.constant dense<[1, 0]> : tensor<2xi32>
// CHECK:           [[VAL_7:%.*]] = "tf.Transpose"([[VAL_3]], [[VAL_6]]) : (tensor<8x40xf32>, tensor<2xi32>) -> tensor<40x8xf32>