code: def convert_q6_k_tensor(
      self, tensor: torch.Tensor, size: int, ndims: int, dims: int
  ):
      # see https://github.com/ggerganov/llama.cpp/blob
      # /8e672efe632bb6a7333964a255c4b96f018b9a65/ggml-quants.c#L2263
  
      block_size = self.block_size[14]
      tensor = tensor.reshape((-1, block_size))
  
      ql, qh, scales, d = (
          tensor[:, :128],
          tensor[:, 128:192],
          tensor[:, 192:208],
          tensor[:, 208:],
      )
      data_0 = (ql[:, 00:32] & 0xF) | ((qh[:, :32] & 0b00000011) << 4)
      data_1 = (ql[:, 32:64] & 0xF) | ((qh[:, :32] & 0b00001100) << 2)
      data_2 = (ql[:, 00:32] >> 4) | ((qh[:, :32] & 0b00110000) >> 0)
      data_3 = (ql[:, 32:64] >> 4) | ((qh[:, :32] & 0b11000000) >> 2)
      data_4 = (ql[:, 64:96] & 0xF) | ((qh[:, 32:64] & 0b00000011) << 4)
      data_5 = (ql[:, 96:128] & 0xF) | (
          (qh[:, 32:64] & 0b00001100) << 2
      )
      data_6 = (ql[:, 64:96] >> 4) | ((qh[:, 32:64] & 0b00110000) >> 0)
      data_7 = (ql[:, 96:128] >> 4) | ((qh[:, 32:64] & 0b11000000) >> 2)
      data = (
          torch.cat(
              [
                  data_0,
                  data_1,
                  data_2,
                  data_3,
                  data_4,
                  data_5,
                  data_6,
                  data_7,
              ],
              dim=-1,
          ).view(torch.int8)
          - 32
      )
      result = data * d.view(torch.half)
  
      result = result.reshape((-1, 16, 16)) * scales.view(
          torch.int8
      ).reshape((-1, 16, 1))
      result = result.reshape(dims)
      return result
  
imports: import struct
  import functools
  import torch
  import numpy
  from io import BufferedReader
  from tqdm import tqdm
  from bigdl.llm.utils.common import invalidInputError
label_desc: Use image node_id 11 for calling the GGUFTensorLoader obj's underlying member method code with example usage: obj.convert_q6_k_tensor(tensor, size, ndims, dims) and returns: result