code: def default_multivariate_normal_fn(
      dtype, shape, name, trainable, add_variable_fn
  ):
      """Creates multivariate standard `Normal` distribution.
  
      Args:
        dtype: Type of parameter's event.
        shape: Python `list`-like representing the parameter's event shape.
        name: Python `str` name prepended to any created (or existing)
          `tf.Variable`s.
        trainable: Python `bool` indicating all created `tf.Variable`s should be
          added to the graph collection `GraphKeys.TRAINABLE_VARIABLES`.
        add_variable_fn: `tf.get_variable`-like `callable` used to create (or
          access existing) `tf.Variable`s.
  
      Returns:
        Multivariate standard `Normal` distribution.
      """
      del name, trainable, add_variable_fn  # unused
      dist = normal_lib.Normal(
          loc=tf.zeros(shape, dtype), scale=dtype.as_numpy_dtype(1)
      )
      batch_ndims = tf.size(dist.batch_shape_tensor())
      return independent_lib.Independent(
          dist, reinterpreted_batch_ndims=batch_ndims
      )
  
file_path: probability/tensorflow_probability/python/layers/util.py
imports: import binascii
  import codecs
  import marshal
  import os
  import types
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python import util
  from tensorflow_probability.python.distributions import deterministic
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 3 for calling a global function with example usage: default_multivariate_normal_fn(dtype, shape, name, trainable, add_variable_fn) and returns: independent_lib