docstr: Returns functional model with Conv2D layer.
docstr_tok: ["Returns","functional","model","with","Conv2D","layer","."]
code: def _get_conv_model(
      self, nr_of_channels, data_format=None, kernel_size=(3, 3)
  ):
      """Returns functional model with Conv2D layer."""
      inp = tf.keras.layers.Input(shape=(32, 32), batch_size=100)
      shape = (
          (1, 32, 32)
          if data_format == "channels_first"
          else (32, 32, 1)
      )
      x = tf.keras.layers.Reshape(shape)(inp)
      x = tf.keras.layers.Conv2D(
          filters=nr_of_channels,
          kernel_size=kernel_size,
          data_format=data_format,
          activation="relu",
      )(x)
      x = tf.keras.layers.MaxPool2D(2, 2)(x)
      out = tf.keras.layers.Flatten()(x)
      model = tf.keras.Model(inputs=inp, outputs=out)
      return model
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.clustering.keras import cluster
  from tensorflow_model_optimization.python.core.clustering.keras import cluster_config
  from tensorflow_model_optimization.python.core.clustering.keras.experimental import cluster
  from tensorflow_model_optimization.python.core.quantization.keras import quantize
  from tensorflow_model_optimization.python.core.quantization.keras.collab_opts.cluster_preserve import default_8bit_cluster_preserve_quantize_scheme
  from tensorflow_model_optimization.python.core.quantization.keras.collab_opts.cluster_preserve.cluster_utils import strip_clustering_cqat
label_desc: Use image node_id 6 for calling the ClusterPreserveIntegrationTest obj's underlying member method code with example usage: obj._get_conv_model(nr_of_channels, data_format, kernel_size) and returns: model