class_docstr: Dummy 'proposal' distribution that just returns samples we pass in.
class_docstr_tok: ["Dummy","'proposal","'","distribution","that","just","returns","samples","we","pass","in","."]
code: def _event_shape(self):
      return tf.nest.map_structure(
          lambda x, nd: tf.TensorShape(x.shape[1 + nd :]),
          self.values_with_sample_dim,
          self.batch_ndims,
      )
  
imports: import collections
  import contextlib
  import functools
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_named
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.experimental.mcmc.particle_filter import infer_trajectories
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 5 for calling the DeterministicEmpirical obj's underlying member method code with example usage: obj._event_shape() and returns: tf