code: def create_base_builder(self, is_updatable=True):
      self.input_features = [("input", datatypes.Array(3))]
      self.output_features = [("output", None)]
      self.output_names = ["output"]
  
      builder = NeuralNetworkBuilder(
          self.input_features, self.output_features
      )
  
      W1 = _np.random.uniform(-0.5, 0.5, (3, 3))
      W2 = _np.random.uniform(-0.5, 0.5, (3, 3))
      builder.add_inner_product(
          name="ip1",
          W=W1,
          b=None,
          input_channels=3,
          output_channels=3,
          has_bias=False,
          input_name="input",
          output_name="hidden",
      )
      builder.add_inner_product(
          name="ip2",
          W=W2,
          b=None,
          input_channels=3,
          output_channels=3,
          has_bias=False,
          input_name="hidden",
          output_name="output",
      )
  
      if is_updatable:
          builder.make_updatable(["ip1", "ip2"])
  
      return builder
  
imports: import os
  import shutil
  import tempfile
  import unittest
  import numpy
  import coremltools.models.datatypes
  from coremltools.models import MLModel
  from coremltools.models.neural_network import AdamParams, NeuralNetworkBuilder, SgdParams, quantization_utils
  from coremltools.models.pipeline import PipelineClassifier, PipelineRegressor
  from coremltools.models.utils import save_spec
label_desc: Use image node_id 3 for calling the MLModelUpdatableTest obj's underlying member method code with example usage: obj.create_base_builder(is_updatable) and returns: builder