module {
  func.func @wrap_composite_func(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
    %0 = "tf.PartitionedCall"(%arg0, %arg1) <{f = @composite_conv2d_with_relu6_fn}> {_tfl_quant_trait = "fully_quantizable"}
          : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    func.return %0 : tensor<*xf32>
  }

  func.func @no_composite_func(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {

      return %2 : tensor<?x2xf32>
    }
    func.func private @composite_dot_general_fn_1(%arg0: tensor<?x2xf32>, %arg1: tensor<2x2xf32>, %arg2: tensor<2xf32>) -> tensor<?x2xf32> attributes {_from_xla_call_module, tf_quant.composite_function} {

  // CHECK-NEXT: tf_mlrt.executeop([[y]]
  %v = "tf.AddV2"(%y, %z) {__op_key = 3: i32}: (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
  return %w, %u, %v : tensor<1xi32>, tensor<1xi32>, tensor<1xi32>
}

// -----

// Test node names are preserved.

// CHECK-LABEL: func @main
func.func @main(%x: tensor<i32>) -> tensor<i32> {
  // CHECK: tf_mlrt.executeop

func.func @squeeze_folder(%arg0 : tensor<?x?xf32>) -> tensor<?x?xf32> {
  %0 = "tfl.squeeze"(%arg0) : (tensor<?x?xf32>) -> tensor<?x?xf32>
  // CHECK: return %arg0
  func.return %0 : tensor<?x?xf32>
}

// -----

// CHECK-LABEL: @squeeze_folder
func.func @squeeze_folder(%arg0 : tensor<?x?xf32>) -> tensor<*xf32> {
  %0 = "tfl.squeeze"(%arg0) : (tensor<?x?xf32>) -> tensor<*xf32>
  // CHECK: "tfl.squeeze"

  func.return %0 : tensor<4xf16>
}

func.func @f32() -> tensor<4xf32> {
  // CHECK-LABEL: @f32
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf32>
  %0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32> } : () -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
}

func.func @f64() -> tensor<4xf64> {
  // CHECK-LABEL: @f64

    func.func @main(%arg0: tensor<2xf32>) -> tensor<2xf32> {
      %0 = "tf.Const"() {value = dense<1.42> : tensor<2xf32>} : () -> tensor<2xf32>
      %1 = "tf.Atan2"(%arg0, %0) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
      func.return %0 : tensor<2xf32>
    }
  })";

  TF_ASSERT_OK(LegalizeModule(kModuleWithConstParam));
}

      return %2 : tensor<?x2xf32>
    }
    func.func private @composite_fn_1(%arg0: tensor<?x2xf32>, %arg1: tensor<2x2xf32>, %arg2: tensor<2xf32>) -> tensor<?x2xf32> attributes {_from_xla_call_module, tf_quant.composite_function} {
      return %arg0 : tensor<?x2xf32>
    }
  }
)mlir";

  return %0 : tensor<*xi32>
}
func.func private @_batched(%arg0: tensor<1xi32>) -> tensor<1xi32> {
  return %arg0 : tensor<1xi32>
}

}

// -----

module attributes {tf_saved_model.semantics} {

// Test hoisting write side-effect ops.

    %1 = "tf.BiasAdd"(%0, %arg2) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>
    %2 = "tf.Relu6"(%1) : (tensor<*xf32>) -> tensor<*xf32>
    func.return %2 : tensor<*xf32>
  }
  func.func private @composite_conv2d_with_bias_and_relu6_fn_1(%arg0: tensor<1x2x2x3xf32>, %arg1: tensor<2x2x3x2xf32>, %arg2: tensor<2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {

  %1 = "tfl.pseudo_const"() { value = dense<1.0> : tensor<16xf32>} : () -> tensor<16xf32>
  %2 = "tfl.conv_3d"(%arg0, %0, %1) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, dilation_d_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32, stride_d = 1 : i32} : (tensor<1x32x32x32x8xf32>, tensor<1x1x1x8x16x!quant.uniform<i8:f32, 1.0>>, tensor<16xf32>) -> tensor<1x32x32x32x16xf32>