%5 = "tfl.quantize"(%4) {qtype = tensor<1x34x1x3x!quant.uniform<i8:f32, 1.0>>, volatile} : (tensor<1x34x1x3xf32>) -> tensor<1x34x1x3x!quant.uniform<i8:f32, 1.0>>
  %6 = "tfl.dequantize"(%5) : (tensor<1x34x1x3x!quant.uniform<i8:f32, 1.0>>) -> tensor<1x34x1x3xf32>

    return %5 : tensor<1x3x1x1xf32>
  }
  func.func private @composite_gather_fn_1(%arg0: tensor<1x3x1x1xf32>, %arg1: tensor<1xi32>, %arg2: tensor<i32>) -> tensor<1x3x1x1xf32> attributes {tf_quant.composite_function} {

    has_token_input_output = false, module = "", platforms = [],
    version = 5 : i64
  } : (tensor<1x3x2x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x2x2x2xf32>
  return %0 : tensor<1x2x2x2xf32>
}

// CHECK-LABEL: func.func @qdq_for_conv_weight_empty
// CHECK-SAME: (%[[ARG_0:.+]]: tensor<1x3x2x3xf32>) -> tensor<1x2x2x2xf32>

  %0 = "tf.Conv2D"(%arg0, %cst) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x3xf32>) -> tensor<1x3x2x3xf32>
  %1 = "tf.AddV2"(%0, %cst_0) : (tensor<1x3x2x3xf32>, tensor<1x3x2x3xf32>) -> tensor<1x3x2x3xf32>
  func.return %1 : tensor<1x3x2x3xf32>
}

func.func @conv3d_no_bias(%arg0: tensor<1x3x4x3x3xf32>) -> (tensor<1x3x2x3x2xf32>, tensor<1x3x2x3x2xf32>, tensor<1x3x2x3x2xf32>) {
  %cst = "tf.Const"() {device = "", value = dense<1.0> : tensor<2x3x3x3x2xf32>} : () -> tensor<2x3x3x3x2xf32>
  %0 = "tf.Conv3D"(%arg0, %cst) {
    data_format = "NDHWC", device = "", dilations = [1, 1, 1, 1, 1], padding = "SAME", strides = [1, 1, 2, 1, 1]
  } : (tensor<1x3x4x3x3xf32>, tensor<2x3x3x3x2xf32>) -> tensor<1x3x2x3x2xf32>

func.func @lift_float_conv3d(%arg0: tensor<1x3x4x3x3xf32>) -> (tensor<1x3x2x3x2xf32>) {
  %cst = "tf.Const"() {device = "", value = dense<1.0> : tensor<2x3x3x3x2xf32>} : () -> tensor<2x3x3x3x2xf32>
  %0 = "tf.Conv3D"(%arg0, %cst) {
    data_format = "NDHWC", device = "", dilations = [1, 1, 1, 1, 1], padding = "SAME", strides = [1, 1, 2, 1, 1]
  } : (tensor<1x3x4x3x3xf32>, tensor<2x3x3x3x2xf32>) -> tensor<1x3x2x3x2xf32>

    %0 = "tf.Div"(%arg0, %cst_2) {device = ""} : (tensor<1x3x4x3x3xf32>, tensor<f32>) -> tensor<1x3x4x3x3xf32>
    %1 = "tf.AddV2"(%0, %cst_0) {device = ""} : (tensor<1x3x4x3x3xf32>, tensor<f32>) -> tensor<1x3x4x3x3xf32>
    %2 = "tf.Floor"(%1) {device = ""} : (tensor<1x3x4x3x3xf32>) -> tensor<1x3x4x3x3xf32>
    %3 = "tf.ClipByValue"(%2, %cst_4, %cst_5) {device = ""} : (tensor<1x3x4x3x3xf32>, tensor<f32>, tensor<f32>) -> tensor<1x3x4x3x3xf32>

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

// CHECK-LABEL: func @multiple_quantizable_ops_in_graph

  %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 %1 : tensor<1x3x4x2xf32>
  }

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