}

func.func @cast(%arg0: tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32> {
  %0 = "tf.Cast"(%arg0) : (tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32>
  func.return %0 : tensor<1x2x2x5xf32>

  // CHECK-LABEL: cast
  // CHECK: "tfl.cast"(%arg0) : (tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32>
}

func.func @castFloat32ToI16(%arg0: tensor<1x2x2x5xf32>) -> tensor<1x2x2x5xi16> {

// Remove TPU related ops.
func.func @tpu_conv(%arg0: tensor<1x3x4x3xf32>) -> tensor<1x3x2x2xf32> {
  %0 = "tf.TPUOrdinalSelector"() {device = ""} : () -> tensor<?xi32>
  %1 = "tf.TPUPartitionedCall"(%arg0, %0) {autotuner_thresh = 0 : i64, device = "", f = @tpu_func_0_optim0} : (tensor<1x3x4x3xf32>, tensor<?xi32>) -> tensor<1x3x2x2xf32>
  %2 = "tf.IdentityN"(%1) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>
  func.return %2 : tensor<1x3x2x2xf32>
}

  %1 = "tf.BiasAdd"(%0, %arg2) <{data_format = "NHWC"}> : (tensor<1x2x2x2xf32>, tensor<2xf32>) -> tensor<1x2x2x2xf32>
  %2 = "tf.Relu6"(%1) {device = ""} : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
  return %2 : tensor<1x2x2x2xf32>
}
// CHECK-LABEL: @composite_conv2d_with_bias_and_relu6_fn_1

func.func @depth_to_space(%arg0: tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> {
  %0 = "tf.DepthToSpace"(%arg0) {block_size = 2: i64,  data_format = "NHWC"}: (tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32>
  func.return %0 : tensor<1x2x2x1xf32>
// CHECK: %[[CUSTOM_0:.*]] = "tfl.custom"(%arg0) <{custom_code = "FlexDepthToSpace", custom_option = #tfl<const_bytes : "{{.*}}">}> : (tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32>
// CHECK: return %[[CUSTOM_0]] : tensor<1x2x2x1xf32>
}

  %res_p3 = "tfl.cumsum"(%arg, %axis_p3) {exclusive = false, reverse = false} : (tensor<1x2x1x3xf32>, tensor<i32>) -> tensor<1x2x1x3xf32>
  func.return %res_m4, %res_m3, %res_m2, %res_m1, %res_00, %res_p1, %res_p2, %res_p3 : tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>

  return %0 : tensor<1x1x2x2xf32>
}
func.func private @XlaCallModule_aten.avg_pool2d.default.impl_4(%arg0: tensor<1x1x3x3xf32>) -> tensor<1x1x2x2xf32>

// CHECK-LABEL: avg_pool2d_5
// CHECK: %cst = arith.constant dense<[0, 2, 3, 1]> : tensor<4xi32>
// CHECK: %0 = "tfl.transpose"(%arg0, %cst) : (tensor<1x1x3x3xf32>, tensor<4xi32>) -> tensor<1x3x3x1xf32>

  %output = "tf.Cast"(%q_output) {Truncate = false} : (tensor<1x2x2x1x!tf_type.qint32>) -> tensor<1x2x2x1xi32>

  // CHECK-DAG: %[[OUTPUT_2:.*]] = "tf.Cast"(%[[OUTPUT_QINT]]) <{Truncate = false}> : (tensor<1x2x2x1x!tf_type.qint32>) -> tensor<1x2x2x1xi32>
  // CHECK-DAG: %[[OUTPUT_QINT_1:.*]] = "tf.Cast"(%[[OUTPUT_1]]) <{Truncate = false}> : (tensor<1x2x2x1xi32>) -> tensor<1x2x2x1x!tf_type.qint32>

  
  %40 = "tfl.read_variable"(%4) : (tensor<!tf_type.resource>) -> tensor<1x2x3xf32>
  %41 = "quantfork.stats"(%40) {layerStats = dense<[0.0, 1.0]> : tensor<2xf32>} : (tensor<1x2x3xf32>) -> tensor<1x2x3xf32>
  %42 = "tfl.concatenation"(%41, %0) {axis = 1 : i32, fused_activation_function = "NONE"} : (tensor<1x2x3xf32>, tensor<1x2x3xf32>) -> tensor<1x4x3xf32>

  %1 = "tf.Identity"(%output_1) {device = ""} : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
  return %1 : tensor<1x2x2x2xf32>
}
// CHECK-LABEL: @serving_default

    return %1 : tensor<1x3x4x2xf32>
  }

  func.func private @composite_conv_fn(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32> attributes {_from_xla_call_module} {