code: def generate_random_rotation_matrix() -> np.ndarray:
      r"""Generates a random rotation matrix.
  
      1. Generate a random unit vector u, randomly sampled from the
        unit sphere (see function generate_random_unit_vector()
        for details)
      2. Generate a second random unit vector v
        a. If absolute value of u \dot v > 0.99, repeat.
          (This is important for numerical stability. Intuition: we
          want them to be as linearly independent as possible or
          else the orthogonalized version of v will be much shorter
          in magnitude compared to u. I assume in Stack they took
          this from Gram-Schmidt orthogonalization?)
        b. v" = v - (u \dot v)*u, i.e. subtract out the component of
          v that's in u's direction
        c. normalize v" (this isn"t in Stack but I assume it must be
          done)
      3. find w = u \cross v"
      4. u, v", and w will form the columns of a rotation matrix, R.
        The intuition is that u, v" and w are, respectively, what
        the standard basis vectors e1, e2, and e3 will be mapped
        to under the transformation.
  
      Returns
      -------
      R: np.ndarray
          A numpy array of shape `(3, 3)`. R is a rotation matrix.
      """
      u = generate_random_unit_vector()
      v = generate_random_unit_vector()
      while np.abs(np.dot(u, v)) >= 0.99:
          v = generate_random_unit_vector()
  
      vp = v - (np.dot(u, v) * u)
      vp /= np.linalg.norm(vp)
      w = np.cross(u, vp)
      R = np.column_stack((u, vp, w))
      return R
  
file_path: deepchem/deepchem/utils/geometry_utils.py
imports: import numpy
  from scipy.spatial.distance import cdist
  from copy import deepcopy
label_desc: Use image node_id 4 for calling a global function with example usage: generate_random_rotation_matrix() and returns: R