%0 = "tf.Sub"(%cst_0, %cst_1) : (tensor<8xf32>, tensor<8xf32>) -> tensor<8xf32>
    return %0 : tensor<8xf32>
  }
// CHECK: @serving_default
// CHECK-DAG: %[[VAR_HANDLE_2:.*]] = "tf.VarHandleOp"() {{.*shared_name = "const_0".*}} : () -> tensor<!tf_type.resource<tensor<8xf32>>>
// CHECK-DAG: %[[READ_VAR_0:.*]] = "tf.ReadVariableOp"(%[[VAR_HANDLE_2]]) : (tensor<!tf_type.resource<tensor<8xf32>>>) -> tensor<8xf32>

  %0 = "tfr.cast"(%arg0) : (tensor<10x!quant.uniform<i8:f32, 0.0039133410900831223:-128>>) -> !tfr.tensor
  %1 = tfr.quant_raw_data(%0) : (!tfr.tensor) -> !tfr.tensor
  %2 = "tfr.cast"(%1) : (!tfr.tensor) -> tensor<10xi8>
  %3 = "tf.Cast"(%2) {Truncate = false} : (tensor<10xi8>) -> tensor<10xi32>
  func.return %3 : tensor<10xi32>

  %cst_1 = arith.constant dense<0> : tensor<4xi32>
  %cst_2 = arith.constant dense<0.000000e+00> : tensor<1xf32>
  %0 = "tfl.quantize"(%cst_2) {qtype = tensor<1x!quant.uniform<i32:f32, 1.0>>, volatile} : (tensor<1xf32>) -> tensor<1x!quant.uniform<i32:f32, 1.0>>
  %1 = "tfl.dequantize"(%0) : (tensor<1x!quant.uniform<i32:f32, 1.0>>) -> tensor<1xf32>
  %cst_3 = arith.constant dense<[[[[1.0]], [[1.0]], [[1.0]]]]> : tensor<1x3x1x1xf32>

    %1 = "tf.AddV2"(%0, %cst_0) {device = ""} : (tensor<?x?x?x?x3xf32>, tensor<f32>) -> tensor<?x?x?x?x3xf32>
    %2 = "tf.Floor"(%1) {device = ""} : (tensor<?x?x?x?x3xf32>) -> tensor<?x?x?x?x3xf32>
    %3 = "tf.ClipByValue"(%2, %cst_5, %cst_6) {device = ""} : (tensor<?x?x?x?x3xf32>, tensor<f32>, tensor<f32>) -> tensor<?x?x?x?x3xf32>

  }

  // CHECK-LABEL: @body(%arg0: tensor<0xf32>) -> tensor<0xf32>
  func.func private @body(%arg0:tensor<0xf32>, %arg1:tensor<*x!tf_type.resource>) -> (tensor<0xf32>, tensor<*x!tf_type.resource>) {
    %val = "tf.ReadVariableOp"(%arg1) : (tensor<*x!tf_type.resource>) -> (tensor<0xf32>)
    %0 = "tf.Mul"(%val, %arg0) : (tensor<0xf32>, tensor<0xf32>) -> tensor<0xf32>
    func.return %0, %arg1 : tensor<0xf32>, tensor<*x!tf_type.resource>
  }

  // NOFALLBACK: XlaSvd
  %s, %u, %v = "tf.XlaSvd"(%arg0) {max_iter = 1, epsilon = 1.0E-09 : f32, precision_config = ""} : (tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>)
  func.return %s, %u, %v : tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>
}

//===----------------------------------------------------------------------===//
// Random op legalizations.

  %2 = chlo.broadcast_add %1, %bias : (
      tensor<?x2x2x1xi32>, tensor<1xi32>) ->tensor<?x2x2x1xi32>
  return %2 : tensor<?x2x2x1xi32>
}

// -----

// CHECK-LABEL: func @convolution_add_add_static
func.func @convolution_add_add_static(
    %lhs: tensor<2x3x2x1xi8>, %rhs: tensor<2x1x1x1xi8>,
    %zp_offset: tensor<2x2x2x1xi32>, %bias: tensor<1xi32>
  ) -> tensor<2x2x2x1xi32> {
  // CHECK-DAG: %[[conv:.*]] = mhlo.convolution

  %7 = "tfl.add"(%2, %1) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<  f32>) -> tensor<4xf32>
  %8 = "tfl.add"(%2, %3) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
  %9 = "tfl.add"(%2, %3) {fused_activation_function = "SIGN_BIT"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>

    tensor type, the shape shouldn't be changed during the conversion.

    Example:

    ```mlir
    %1 = tfr.constant_tensor(%0) : f32 -> tensor<f32>
    %3 = tfr.constant_tensor(%2) : vector<1xf32> -> tensor<1xf32>
    ```
  }];

  let arguments = (ins TFR_AllAttrTypes:$arg);

  let results = (outs TFR_singleTensorType:$out);

  let hasCanonicalizer = 1;

  let hasVerifier = 1;
}

  %outputs_50 =  "tf.Mul"(%outputs_30, %outputs_48) {device = ""} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  %outputs_52 =  "tf.Reshape"(%outputs_50, %outputs_0) {device = ""} : (tensor<*xf32>, tensor<2xi32>) -> tensor<*xf32>
  %outputs_54 =  "tf.MatMul"(%outputs_40, %outputs_52) {device = "", transpose_a = false, transpose_b = false} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>