// Compute Mean over spatial dimensions in NHWC format.
  %2 = "tf.Const"() {value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
  %3 = "tf.Mean"(%1, %2) : (tensor<1x112x112x64xf32>, tensor<2xi32>) -> tensor<1x64xf32>

  func.return %3 : tensor<1x64xf32>
}

// CHECK-LABEL: func @fold_into_fused_batch_norm

  %cst = arith.constant dense<1.0> : tensor<4xf32>
  %cst_3 = arith.constant dense<2.0> : tensor<4x3x3x3xf32>
  %0 = "tfl.conv_2d"(%arg0, %cst_3, %cst) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU6", padding = "VALID", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<?x19x19x3xf32>, tensor<4x3x3x3xf32>, tensor<4xf32>) -> tensor<?x9x9x4xf32>
  func.return %0 : tensor<?x9x9x4xf32>

  // CHECK-NEXT:  buffer: 6
  // CHECK-NEXT:  } ]
  // CHECK-NEXT:  signature_defs: [ ]
  // CHECK-NEXT:}

  %0 = "tfl.pseudo_const" () {value = dense<-1.23697901> : tensor<32xf32>} : () -> tensor<32xf32> loc("Const")

    tf_executor.graph {
      %0:2 = tf_executor.island wraps "tf.Const"() {device = "TPU:0", name = "const", dtype = "tfdtype$DT_INT32", value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
      tf_executor.fetch
    }
    func.return
  }
}

// CHECK-NOT: node

// CHECK: library
// CHECK-NEXT: function
// CHECK-NEXT: signature
// CHECK-NEXT: name: "main"

  %handle = "tf.VarHandleOp"() {device = "/device:CPU:0", container = "", shared_name = "x"} : () -> tensor<!tf_type.resource<tensor<3xf32>>>
  %0 = "tf.ReadVariableOp"(%handle) {_output_shapes = ["tfshape$dim { size: 3 }"], device = "/device:CPU:0", dtype = f32} : (tensor<!tf_type.resource<tensor<3xf32>>>) -> tensor<3xf32>

module {
func.func @main(%arg0: tensor<21x32x32x128xf32>, %arg1: tensor<2xi32>) -> tensor<1x64x64x128xf32> {
  %0 = "tf.ResizeBilinear"(%arg0, %arg1) {align_corners = false, device = "", half_pixel_centers = true} : (tensor<21x32x32x128xf32>, tensor<2xi32>) -> tensor<1x64x64x128xf32>
  func.return %0 : tensor<1x64x64x128xf32>
}

      %c = stablehlo.constant dense<40> : tensor<2xi32>
      %0 = "tf.UnconvertedOp"(%arg0, %c) {align_corners = false, half_pixel_centers = false} : (tensor<1x20x20x28xf32>, tensor<2xi32>) -> tensor<1x40x40x28xf32>
      func.return %0 : tensor<1x40x40x28xf32>
  }

    tf_executor.fetch
  }
  func.return

    func.return
}

func.func private @__reduce_func_1(%arg0: tensor<i64> {tf._user_specified_name = "args_0"}, %arg1: tensor<32xf32> {tf._user_specified_name = "args_1"}) -> (tensor<i64>) attributes {tf._tf_data_function = true, tf.signature.is_stateful} {
    %0 = "tf.JustPretend"(%arg1) : (tensor<32xf32>) -> (tensor<i64>)
    func.return %0 : tensor<i64>
}

// CHECK-LABEL: func @single_state_single_dataset_type_multiple_arguments

  %0 = "tf.AddV2"(%input, %input) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>

  // CHECK: "mhlo.dot"
  // CHECK-SAME: (tensor<?x?xf32>, tensor<2x2x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<?x?xf32>