class_docstr: Base class for testing Tangent Spaces.
class_docstr_tok: ["Base","class","for","testing","Tangent","Spaces","."]
code: def _testSpecializations(
      self, bijector_class, event_ndims, bijector_params
  ):
      coords = self.generate_coords()
      z = self.embed_coords(coords)
      bijector = bijector_class(**bijector_params)
      tangent_space = self.tangent_space(z)
      log_volume1, _ = tangent_space.transform_general(
          z, bijector, event_ndims=event_ndims
      )
      log_volume2, _ = tangent_space.transform_dimension_preserving(
          z, bijector, event_ndims=event_ndims
      )
      log_volume3, _ = tangent_space.transform_coordinatewise(
          z, bijector, event_ndims=event_ndims
      )
  
      (log_volume1, log_volume2, log_volume3) = self.evaluate(
          [log_volume1, log_volume2, log_volume3]
      )
      self.assertAllClose(log_volume1, log_volume2)
      self.assertAllClose(log_volume1, log_volume3)
  
file_path: probability/tensorflow_probability/python/experimental/tangent_spaces/spaces_test_util.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.experimental.tangent_spaces import spaces
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 2 for calling the SpacesTest obj's underlying member method code with example usage: obj._testSpecializations(bijector_class, event_ndims, bijector_params) without return types