Output x = Placeholder(scope_, DT_FLOAT);
  NameAttrList f;
  f.set_name("XTimesTwo");
  (*f.mutable_attr())["T"].set_type(DT_FLOAT);
  auto results =
      PartitionedCall(scope_, std::initializer_list<Input>{x}, {DT_FLOAT}, f);
  RunTest(x, {}, results[0], {});

  auto stateful_results = StatefulPartitionedCall(
      scope_, std::initializer_list<Input>{x}, {DT_FLOAT}, f);

  Output in = ops::Placeholder(root, DT_FLOAT);
  Output perm = ops::Const(root, {3, 1, 2, 0});

  NameAttrList b_name_attr;
  b_name_attr.set_name("TransposeCall");
  ops::PartitionedCall call(root.WithOpName("call"), {in, perm}, {DT_FLOAT},
                            b_name_attr);
  call.output.front().node()->AddAttr(kXlaMustCompileAttr, true);

  std::unique_ptr<Graph> graph;

  }
  attr {
    key: "shape"
    value {
      shape {
      }
    }
  }
  attr {
    key: "shared_name"
    value {
      s: ""
    }
  }
}
node {
  name: "Call"
  op: "PartitionedCall"
  attr {
    key: "Tin"
    value {
      list {
      }
    }
  }
  attr {
    key: "Tout"
    value {
      list {
        type: DT_RESOURCE
      }
    }
  }
  attr {

  auto value = device_attr.getValue();
  // TODO(b/229028654): Remove string conversion once we have C++17.
  absl::string_view device_type(value.data(), value.size());
  // Device type may be empty for some ops, e.g. tf.PartitionedCall.
  auto it = std::find(kValidDeviceTypes.begin(), kValidDeviceTypes.end(),
                      device_type);
  if (it == kValidDeviceTypes.end()) return failure();
  return success();
}

      }
    }
  }
  attr {
    key: "Tout"
    value {
      list {
        type: DT_FLOAT
      }
    }
  }
  attr {
    key: "_gradient_op_type"
    value {
      s: "PartitionedCall-1205"
    }
  }
  attr {
    key: "config"
    value {
      s: ""
    }
  }
  attr {
    key: "config_proto"
    value {

  // CHECK-NOT: Optional
  %0 = "tf.OptionalFromValue"(%arg0) : (tensor<f32>) -> tensor<!tf_type.variant<tensor<f32>>>
  %1 = "tf.PartitionedCall"(%0) {
      config = "",
      config_proto = "",
      executor_type = "",
      f = @identity} : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>

      return failure();
    }
    rewriter.setInsertionPointAfter(call_op);

    // The TPUPartitionedCall has a TPUOrdinalSelectorOp for its last argument
    // which should be removed. So the replaced PartitionedCall op should keep
    // its original arguments except for the last element.
    SmallVector<Value> args = call_op.getOperands().drop_back();

    rewriter.replaceOpWithNewOp<TF::PartitionedCallOp>(

    .SetShapeFn([](InferenceContext* c) {
      c->set_output(0, c->input(0));
      return absl::OkStatus();
    })
    .Doc(R"(XLA Merge Op. For use by the XLA JIT only.

Merges the outputs from the PartitionedCall node and the _XlaRun node.
Unlike the TensorFlow Merge op, which requires inputs of some types to be
placed on the host, the _XlaMerge op can merge inputs of all types when

// CHECK-NEXT: [[q_bias:%.+]] = "quantfork.qcast"([[bias]]) : (tensor<2xf32>) -> tensor<2x!quant.uniform<i32:f32, 0.044022349891595126>>

      return true;
    }
  }
  return false;
}

// Checks if the function is only used by supported ops. Returns false when the
// function has no uses. Currently, only PartitionedCall is supported.
// TODO(b/284222309): Support lifting for functions called by control flow.
bool UsedBySupportedOps(ModuleOp module, func::FuncOp func) {
  auto function_uses =