code: def forward(self, x: torch.Tensor):
      # only work for GPU
      invalidInputError(
          x.device.type == "xpu", "FP16Linear only works for Intel GPUs"
      )
      x = x.to(torch.float16)
      if self.bias is not None and self.bias.dtype != x.dtype:
          self.bias.data = self.bias.data.to(x.dtype)
      if self.weight is not None and self.weight.dtype != x.dtype:
          self.weight.data = self.weight.data.to(x.dtype)
  
      if not self.use_esimd_kernel(x):
          if get_ipex_version() < "2.1.10+xpu":
              if self.weight_type == 2:
                  self.weight = self.weight.transpose(0, 1).contiguous()
                  self.weight_type = 1
              return F.linear(x, self.weight, self.bias)
          else:
              if self.weight_type == 1:
                  self.weight = self.weight.transpose(0, 1).contiguous()
                  self.weight_type = 2
              return torch.ops.torch_ipex.matmul_bias_out(
                  x, self.weight, self.bias
              )
      else:
          if self.weight_type != 3:
              # convert weight first to use esimd fp16 kernel
              self.convert_weight_for_esimd_kernel()
          # esimd fp16 kernel for inference
          x_shape = x.shape
          x_2d = x.view(-1, x_shape[-1])
          if x_2d.is_contiguous() is False:
              x_2d = x_2d.contiguous()
  
          try:
              import intel_extension_for_pytorch
              import linear_fp16_esimd
          except ModuleNotFoundError:
              invalidInputError(
                  False,
                  "Please `pip install bigdl_core_xe_esimd` first.",
              )
  
          if x_2d.shape[0] > 1:
              # first token or batch size > 1, re-convert weight
              original_weight = self.weight.data.transpose(1, 2)
              original_weight = original_weight.reshape(
                  self.out_len, self.in_len
              )
              result = F.linear(x_2d, original_weight.contiguous())
              del original_weight
          else:
              # rest token, use esimd optimization
              result = linear_fp16_esimd.forward(x_2d, self.weight.data)
  
          new_shape = x_shape[:-1] + (self.out_len,)
          result = result.view(new_shape)
          if self.mp_group is not None:
              from deepspeed import comm as dist
  
              dist.inference_all_reduce(result, group=self.mp_group)
          if self.bias is not None:
              result += self.bias
  
          return result.to(x.dtype)
  
imports: from typing import Optional, TypeVar, Union, overload
  from bigdl.llm.utils.common import invalidInputError
  import os
  import torch
  import torch.nn.functional
  from torch import Tensor, device, dtype, nn
  from operator import mul
  from functools import reduce
  from bigdl.llm.transformers.xpu_customize_fwd import custom_fwd, custom_bwd
  from bigdl.llm.transformers.utils import get_autocast_dtype, get_xpu_device_type, get_ipex_version
  import bigdl.llm.ggml.model.llama.llama_cpp
  import ctypes
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
label_desc: Use image node_id 2 for calling the FP16Linear obj's underlying member method code with example usage: obj.forward(x) and returns: result, F, torch