// CHECK-SAME: (tensor<4x3x6x5xf32>, tensor<4x3x5x2xf32>) -> tensor<4x3x6x2xf32>
  // CHECK: return %[[CALL]]

  func.func private @composite_dot_general_fn(%arg0: tensor<4x3x6x5xf32>, %arg1: tensor<4x3x5x2xf32>) -> tensor<4x3x6x2xf32> attributes {_from_xla_call_module} {
    %0 = stablehlo.dot_general %arg0, %arg1, batching_dims = [0, 1] x [0, 1], contracting_dims = [3] x [2] : (tensor<4x3x6x5xf32>, tensor<4x3x5x2xf32>) -> tensor<4x3x6x2xf32>

func.func @ConvertSliceToIdentityI32(%arg0: tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> {
  %begin = arith.constant dense<0> : tensor<4xi32>
  %shape = arith.constant dense<[2,3,4,5]> : tensor<4xi32>
  %0 = "tfl.slice"(%arg0, %begin, %shape) : (tensor<2x3x4x5xf32>, tensor<4xi32>, tensor<4xi32>) -> tensor<2x3x4x5xf32>
  func.return %0 : tensor<2x3x4x5xf32>
  // CHECK: return %arg0
}

// 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>

        %0 = stablehlo.constant dense<2.000000e+00> : tensor<3x3x4x4xf32>
        %1 = stablehlo.convolution(%arg0, %0) 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<1x3x3x4xf32>, tensor<3x3x4x4xf32>) -> tensor<1x3x3x4xf32>

// 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>

}

func.func @LRN(%arg0: tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> {
  %0 = "tf.LRN"(%arg0) {depth_radius = 5 :i64, bias = 1.0 :f32, alpha = 1.0 : f32, beta = 0.5 :f32} : (tensor<2x3x4x5xf32>) -> (tensor<2x3x4x5xf32>)
  func.return %0: tensor<2x3x4x5xf32>

  // CHECK-LABEL: LRN

    %9 = stablehlo.convert %3 : (tensor<3x3x4x4xi8>) -> tensor<3x3x4x4xf32>
    %10 = stablehlo.convolution(%8, %9) 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<1x3x3x4xf32>, tensor<3x3x4x4xf32>) -> tensor<1x3x3x4xf32>
    %11 = stablehlo.reshape %2 : (tensor<1x1x1x1xi8>) -> tensor<1xi8>

    %6 = "tf.Div"(%arg1, %cst_2) {device = ""} : (tensor<2x3x4x5xf32>, tensor<f32>) -> tensor<2x3x4x5xf32>
    %7 = "tf.AddV2"(%6, %cst_1) {device = ""} : (tensor<2x3x4x5xf32>, tensor<f32>) -> tensor<2x3x4x5xf32>
    %8 = "tf.Maximum"(%7, %cst_1) {device = ""} : (tensor<2x3x4x5xf32>, tensor<f32>) -> tensor<2x3x4x5xf32>
    %9 = "tf.Minimum"(%8, %cst_4) {device = ""} : (tensor<2x3x4x5xf32>, tensor<f32>) -> tensor<2x3x4x5xf32>

  return %0 : tensor<1x1x1x5xf32>
}
func.func private @XlaCallModule_aten.avg_pool2d.default.impl_6(%arg0: tensor<1x1x1x8xf32>) -> tensor<1x1x1x5xf32>

// CHECK-LABEL: avg_pool2d_7
// CHECK: %cst = arith.constant dense<[0, 2, 3, 1]> : tensor<4xi32>

    %3 = "tf.Identity"(%2) {device = ""} : (tensor<1x3x1x1xf32>) -> tensor<1x3x1x1xf32>
    return %3 : tensor<1x3x1x1xf32>
  }

// CHECK-LABEL: func @multiple_quantizable_ops_in_graph