code: def test_bfgs_inverse_hessian(dim, history):
      position_diffs = jnp.ones((dim, history))
      gradient_diffs = jnp.ones((dim, history))
      alpha, beta, gamma = pathfinder.bfgs_inverse_hessian(
          updates_of_position_differences=position_diffs,
          updates_of_gradient_differences=gradient_diffs,
      )
      assert alpha.shape == (dim,)
      assert beta.shape == (dim, 2 * history)
      assert gamma.shape == (2 * history, 2 * history)
  
file_path: probability/discussion/pathfinder/pathfinder_test.py
imports: import jax
  import jax.numpy
  import numpy
  import pytest
  from discussion.pathfinder import pathfinder
  import tensorflow_probability.substrates.jax
label_desc: Use image node_id 1 for calling a global function with example usage: test_bfgs_inverse_hessian(dim, history) without return types