code: def _dummy_data() -> Tuple[InputData, InputData, OutputData]:
      X = tf.convert_to_tensor(
          np.random.randn(100, INPUT_DIM), dtype=tf.float64
      )
      Y = tf.convert_to_tensor(
          np.random.randn(100, OUTPUT_DIM), dtype=tf.float64
      )
      X_new = tf.convert_to_tensor(
          np.random.randn(100, INPUT_DIM), dtype=tf.float64
      )
  
      return X, X_new, Y
  
file_path: GPflow/tests/gpflow/models/test_sgpr_posterior.py
imports: from typing import Tuple
  import numpy
  import pytest
  import tensorflow
  from check_shapes import check_shapes
  import gpflow
  from gpflow.base import InputData, OutputData
  from gpflow.inducing_variables import InducingPoints
  from gpflow.models.sgpr import SGPR, SGPR_deprecated
  from gpflow.posteriors import PrecomputeCacheType
label_desc: Use image node_id 1 for calling a global function with example usage: _dummy_data() and returns: X,  X_new,  Y