class_docstr: The 1x1 Conv bijector used in Glow.
  
  This class has a convenience function which initializes the parameters
  of the bijector.
class_docstr_tok: ["The","1x1","Conv","bijector","used","in","Glow",".","This","class","has","a","convenience","function","which","initializes","the","parameters","of","the","bijector","."]
code: def trainable_lu_factorization(
      event_size, seed=None, dtype=tf.float32, name=None
  ):
      with tf.name_scope(name or "trainable_lu_factorization"):
          event_size = tf.convert_to_tensor(
              event_size, dtype_hint=tf.int32, name="event_size"
          )
          random_matrix = tf.random.uniform(
              shape=[event_size, event_size], dtype=dtype, seed=seed
          )
          random_orthonormal = tf.linalg.qr(random_matrix)[0]
          lower_upper, permutation = tf.linalg.lu(random_orthonormal)
          lower_upper = tf.Variable(
              initial_value=lower_upper,
              trainable=True,
              name="lower_upper",
          )
          # Initialize a non-trainable variable for the permutation indices so
          # that its value isn't re-sampled from run-to-run.
          permutation = tf.Variable(
              initial_value=permutation,
              trainable=False,
              name="permutation",
          )
          return lower_upper, permutation
  
imports: import functools
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import blockwise
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import composition
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import real_nvp
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import scale_matvec_lu
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import transpose
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.util.deferred_tensor import TransformedVariable
  from tensorflow_probability.python.util.seed_stream import SeedStream
label_desc: Use image node_id 6 for calling the OneByOneConv obj's underlying member method code with example usage: obj.trainable_lu_factorization(event_size, seed, dtype, name) and returns: lower_upper,  permutation