code: def _dequantize_linear(weight_8bit, scale, bias, axis=0):
      if (
          len(weight_8bit.shape) == 1
      ):  # vector situation, treat as 1 channel
          weight_8bit = weight_8bit.reshape((1, weight_8bit.shape[0]))
  
      rank = len(weight_8bit.shape)
      if axis == 1:
          transposed_axis_order = (1, 0) + tuple(range(2, rank))
          weight_8bit = _np.transpose(
              weight_8bit, transposed_axis_order
          )
  
      num_channels = weight_8bit.shape[0]
      broadcast_shape = (num_channels,) + (1,) * (rank - 1)
      scale = scale.reshape(broadcast_shape)
      bias = bias.reshape(broadcast_shape)
      weight = weight_8bit.astype("float") * scale + bias
      if axis == 1:
          weight = _np.transpose(weight, transposed_axis_order)
  
      return weight
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import numpy
  from sys import stdout
  from os import listdir
  from six import string_types
  from .optimization_utils import _optimize_nn
  from coremltools.models import _SUPPORTED_QUANTIZATION_MODES, _QUANTIZATION_MODE_DEQUANTIZE, _QUANTIZATION_MODE_LOOKUP_TABLE_LINEAR, _QUANTIZATION_MODE_LOOKUP_TABLE_KMEANS, _QUANTIZATION_MODE_CUSTOM_LOOKUP_TABLE, _QUANTIZATION_MODE_LINEAR_QUANTIZATION, _QUANTIZATION_MODE_LINEAR_SYMMETRIC, _LUT_BASED_QUANTIZATION
  from ..utils import _get_nn_layers, _wp_to_fp16wp, _get_model, _macos_version
  from ..._deps import _HAS_SKLEARN
  from ...None import _MINIMUM_QUANTIZED_MODEL_SPEC_VERSION, _MINIMUM_FP16_SPEC_VERSION, _SPECIFICATION_VERSION_IOS_14
label_desc: Use image node_id 10 for calling a global function with example usage: _dequantize_linear(weight_8bit, scale, bias, axis) and returns: weight