code: def test_linear_quant_convolution_8bit_float_scale_and_bias(self):
      W = np.array(([[[[1, 248], [248, 248]]]]), dtype=np.uint8)
      mlmodel = self.build_quant_conv_layer(
          W=W.flatten().tobytes(),
          quantization_type="linear",
          nbits=8,
          quant_scale=[15],
          quant_bias=[-3913],
          output_channels=1,
      )
      data = np.ones((1, 2, 2))
      data_dict = {"data": data}
      out = mlmodel.predict(data_dict)["out"]
      # Output should be equal to: (scale*(1+248+248+248)+(4*bias))
      expected_out = np.reshape(np.array([-4477]), (1, 1, 1, 1, 1))
      self.assertTrue(np.allclose(out, expected_out))
  
imports: import numpy
  import pytest
  import unittest
  import coremltools
  from coremltools import ComputeUnit
  from coremltools.converters.mil.mil.types.type_mapping import np_val_to_py_type
  from coremltools.models import MLModel, datatypes
  from coremltools.models.neural_network import NeuralNetworkBuilder
  from coremltools.models.neural_network.quantization_utils import _convert_array_to_nbit_quantized_bytes, quantize_weights
  from coremltools.models.utils import _is_macos, _macos_version
label_desc: Use image node_id 4 for calling the BasicNumericCorrectnessTest_1014NewLayers obj's underlying member method code with example usage: obj.test_linear_quant_convolution_8bit_float_scale_and_bias() without return types