docstr: Initialize the test class.
docstr_tok: ["Initialize","the","test","class","."]
code: def __init__(self, methodname):
      """Initialize the test class."""
      super().__init__(methodname)
  
      self.ntrain, self.ntest = 50, 100
      self.nclass = 5
      self.ndim = 10
  
      # Generate random training and test data
      self.train_data = np.random.rand(self.ntrain, self.ndim)
      self.test_data = np.random.rand(self.ntest, self.ndim)
      self.train_labels = np.random.randint(
          self.nclass, size=self.ntrain
      )
      self.test_labels = np.random.randint(self.nclass, size=self.ntest)
      self.sample_weight_train = np.random.rand(self.ntrain)
      self.sample_weight_test = np.random.rand(self.ntest)
  
      # Define a simple model function
      def model_fn(features, labels, mode):
          """Model function for logistic regression."""
          del labels
  
          input_layer = tf.reshape(features["x"], [-1, self.ndim])
          logits = tf.keras.layers.Dense(self.nclass)(input_layer)
  
          # Define the PREDICT mode becasue we only need that
          if mode == tf_estimator.ModeKeys.PREDICT:
              predictions = tf.nn.softmax(logits)
              return tf_estimator.EstimatorSpec(
                  mode=mode, predictions=predictions
              )
  
      # Define the classifier, input_fn for training and test data
      self.classifier = tf_estimator.Estimator(model_fn=model_fn)
      self.input_fn_train = (
          tf_compat_v1_estimator.inputs.numpy_input_fn(
              x={"x": self.train_data},
              y=self.train_labels,
              num_epochs=1,
              shuffle=False,
          )
      )
      self.input_fn_test = tf_compat_v1_estimator.inputs.numpy_input_fn(
          x={"x": self.test_data},
          y=self.test_labels,
          num_epochs=1,
          shuffle=False,
      )
  
file_path: privacy/tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/tf_estimator_evaluation_test.py
imports: from absl.testing import absltest
  import numpy
  import tensorflow
  from tensorflow import estimator
  from tensorflow.compat.v1 import estimator
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import data_structures
  from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import tf_estimator_evaluation
label_desc: Use image node_id 1 to create a new UtilsTest object from inherited base classes: absltest with example: obj = UtilsTest(methodname)