code: def _sanitize_testing_args(batch_input_shape, batch_input_data):
      """Construct appropriate values for input_shape and input_data whenever one
      is missing."""
      if batch_input_data is None:
          if batch_input_shape is None:
              raise ValueError("input_shape is None")
          input_data_shape = list(batch_input_shape)
          for i, e in enumerate(input_data_shape):
              if e is None:
                  input_data_shape[i] = 2
          batch_input_data = 10 * np.random.random(input_data_shape)
      elif batch_input_shape is None:
          batch_input_shape = batch_input_data.shape
      return batch_input_shape, batch_input_data
  
imports: import numpy
  import tensorflow
  import tf_encrypted
  from tf_encrypted.keras.models import Sequential
label_desc: Use image node_id 3 for calling a global function with example usage: _sanitize_testing_args(batch_input_shape, batch_input_data) and returns: batch_input_shape,  batch_input_data