code: def _random_variance(dim, batch, dtype, seed):
      w = tf.random.stateless_normal(
          batch + (dim, dim), dtype=dtype, seed=seed
      )
      return tf.eye(dim, dtype=dtype) + 0.1 * tf.linalg.matmul(
          w, w, transpose_a=True
      )
  
imports: import collections
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import linear_gaussian_ssm
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.experimental.parallel_filter import parallel_kalman_filter_lib
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.stats import sample_stats
label_desc: Use image node_id 1 for calling a global function with example usage: _random_variance(dim, batch, dtype, seed) and returns: unknown