code: def log_local_area(self, local_grads):
      log_volume = 0.5 * tf.linalg.logdet(
          tf.linalg.matmul(local_grads, local_grads, transpose_b=True)
      )
      # We need to account for the fact the measure on the probability simplex is
      # not the lebesgue measure, and differs by a constant.
      # For instance, the length of the line segment from (0, 1) to (1, 0) is
      # sqrt(2), but is 1 under the simplex measure.
      return (
          self.dims * np.log(self.delta)
          + log_volume
          - 0.5 * np.log1p(self.dims)
      )
  
imports: from absl.testing import parameterized
  import numpy
  import scipy.special
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import scale_matvec_tril
  from tensorflow_probability.python.experimental.tangent_spaces import simplex
  from tensorflow_probability.python.experimental.tangent_spaces import spaces_test_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 4 for calling the _SimplexTest obj's underlying member method code with example usage: obj.log_local_area(local_grads) and returns: unknown