code: def _det_large_enough_mask(x, det_bounds):
      """Returns whether the input matches the given determinant limit.
  
      Args:
        x: A floating-point `Tensor` of shape `[B1, ..., Bn, M, M]`.
        det_bounds: A floating-point `Tensor` that must broadcast to shape
          `[B1, ..., Bn]`, giving the desired lower bound on the
          determinants in `x`.
  
      Returns:
        mask: A floating-point `Tensor` of shape [B1, ..., Bn].  Each
          scalar is 1 if the corresponding matrix had determinant above
          the corresponding bound, otherwise 0.
      """
      # For the curious: I wonder whether it is possible and desirable to
      # use a Cholesky decomposition-based algorithm for this, since the
      # only matrices whose determinant this code cares about will be PSD.
      # Didn't figure out how to code that in TensorFlow.
      #
      # Expert opinion is that it would be about twice as fast since
      # Cholesky is roughly half the cost of Gaussian Elimination with
      # Partial Pivoting. But this is less of an impact than the switch in
      # _psd_mask.
      return tf.cast(tf.linalg.det(x) > det_bounds, dtype=x.dtype)
  
file_path: probability/tensorflow_probability/python/distributions/internal/correlation_matrix_volumes_lib.py
imports: import sys
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import empirical_statistical_testing
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.math.linalg import fill_triangular
label_desc: Use image node_id 2 for calling a global function with example usage: _det_large_enough_mask(x, det_bounds) and returns: tf