data_format = "NCHW",
         epsilon = 1.001000e-05 : f32,
         is_training = false
       } : (tensor<?x64x112x112xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>)
        -> (tensor<?x64x112x112xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<*xf32>)

  // CHECK: "tf.FusedBatchNormV3"
  // CHECK-SAME: data_format = "NHWC"

         exponential_avg_factor = 1.0 : f32,
         is_training = true
       }
        : (tensor<1x28x28x64xf32>, tensor<64xf32>, tensor<64xf32>,
           tensor<64xf32>, tensor<64xf32>)
       -> (tensor<1x28x28x64xf32>, tensor<64xf32>, tensor<64xf32>,
           tensor<64xf32>, tensor<64xf32>, tensor<64xf32>)

  func.return %y : tensor<1x28x28x64xf32>
}

// CHECK-LABEL: func @transposeFusedBatchNormGradV3

         exponential_avg_factor = 1.0 : f32,
         is_training = true
       }
        : (tensor<1x64x28x28xf32>, tensor<64xf32>, tensor<64xf32>,
           tensor<64xf32>, tensor<64xf32>)
       -> (tensor<1x64x28x28xf32>, tensor<64xf32>, tensor<64xf32>,
           tensor<64xf32>, tensor<64xf32>, tensor<64xf32>)

  func.return %y : tensor<1x64x28x28xf32>
}

// CHECK-LABEL: bias_add_nchw

         exponential_avg_factor = 1.0 : f32,
         is_training = false
       }
        : (tensor<1x112x112x64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>)
       -> (tensor<1x112x112x64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>)

  func.return %2#0 : tensor<1x112x112x64xf32>
}

// CHECK-LABEL: func @fold_into_pad_with_extra_uses

    -> (tensor<i64>, tensor<i32>) attributes {tf._tf_data_function = true, tf.signature.is_stateful} {
    %0:2 = "tf.JustPretend"(%arg2, %arg3, %arg4, %arg5) : (tensor<32xf32>, tensor<64xf64>, tensor<!tf_type.resource<tensor<64xf32>>>, tensor<!tf_type.resource<tensor<128xf32>>>) -> (tensor<i64>, tensor<i32>)
    func.return %0#0, %0#1: tensor<i64>, tensor<i32>

  func.return %0 : tensor<256x32x32x16xf32>
}

func.func @testConv2DDynamicShape(tensor<?x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<?x32x32x16xf32> {
^bb0(%arg0: tensor<?x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>):
  // CHECK: _arithmetic_count = -1 : i64

  // Confirm that attributes that cannot be stored in the flatbuffer options
  // for a given operator are dropped silently.
  %1 = "tfl.squared_difference"(%arg0, %0) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("squared_difference")
  %2 = "tfl.mul"(%arg0, %1) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("mul")

    %add = "tf.AddV2"(%call, %call) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
    "tf.Yield"(%call, %add) : (tensor<4xf32>, tensor<4xf32>) -> ()
  },  {
    %call_0 = func.call @test_if_else1(%arg1) : (tensor<4xf32>) -> tensor<4xf32>
    "tf.Yield"(%call_0, %call_0) : (tensor<4xf32>, tensor<4xf32>) -> ()
  }) {is_stateless = false} : (tensor<i1>) -> (tensor<*xf32>, tensor<4xf32>)

// CHECK-NEXT:   "tfl.yield"(%[[MY_CUSTOM]]) : (tensor<4xf32>) -> ()
// CHECK-NEXT: }) {fused_activation_function = "RELU", int_attr = 2 : i32} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
// CHECK-NEXT: %[[CUSTOM_2:.*]] = "tfl.custom_tf"(%[[MUL]], %[[CST]]) ({
// CHECK-NEXT: ^bb0(%arg1: tensor<4xf32>, %arg2: tensor<4xf32>):

// CHECK-SAME: (%[[ARG0:.*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)
func.func @testReadVariableOpColocated(%arg0: tensor<*x!tf_type.resource<tensor<4xf32>>>) {
  // CHECK:      tf_device.replicate
  // CHECK-SAME: (%[[ARG0]] as %[[RI_0:[a-z0-9]*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)
  tf_device.replicate(%arg0 as %arg1: tensor<*x!tf_type.resource<tensor<4xf32>>>) {
    _mirrored_variable_indices = [0],