class_docstr: Quantization tests for dynamic Int8 - Int8 matrix multiplications
class_docstr_tok: ["Quantization","tests","for","dynamic","Int8","-","Int8","matrix","multiplications"]
code: def initialize(self):
      np.random.seed(1988)
      self.Cout, self.Cin = 16, 32
      self.W = np.random.rand(self.Cout, self.Cin) * 20.0 - 10.0
      self.b = np.random.rand(self.Cout) * 20.0 - 10.0
      self.input_shape = (5, self.Cin)
      input_features = [("data", datatypes.Array(*self.input_shape))]
      output_features = [("output", None)]
      self.builder = neural_network.NeuralNetworkBuilder(
          input_features,
          output_features,
          disable_rank5_shape_mapping=True,
      )
      self.selector = MatrixMultiplyLayerSelector()
  
imports: import itertools
  import unittest
  import numpy
  import pytest
  import coremltools
  import coremltools.models.datatypes
  from coremltools import ComputeUnit
  from coremltools.models import _QUANTIZATION_MODE_LINEAR_QUANTIZATION, neural_network
  from coremltools.models.neural_network import quantization_utils
  from coremltools.models.neural_network.quantization_utils import MatrixMultiplyLayerSelector, _quantize_spec_weights, activate_int8_int8_matrix_multiplications
label_desc: Use image node_id 1 for calling the DynamicQuantizedInt8Int8MatMul obj's underlying member method code with example usage: obj.initialize() without return types