class_docstr: Runs one step of Uncalibrated Langevin discretized diffusion.
  
  The class generates a Langevin proposal using `_euler_method` function and
  also computes helper `UncalibratedLangevinKernelResults` for the next
  iteration.
  
  Warning: this kernel will not result in a chain which converges to the
  `target_log_prob`. To get a convergent MCMC, use
  `MetropolisAdjustedLangevinAlgorithm(...)` or
  `MetropolisHastings(UncalibratedLangevin(...))`.
  
  For more details on `UncalibratedLangevin`, see
  `MetropolisAdjustedLangevinAlgorithm`.
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code: def experimental_with_shard_axes(self, shard_axis_names):
      return self.copy(experimental_shard_axis_names=shard_axis_names)
  
imports: import collections
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import distribute_lib
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.math.diag_jacobian import diag_jacobian
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.mcmc import metropolis_hastings
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 13 for calling the UncalibratedLangevin obj's underlying member method code with example usage: obj.experimental_with_shard_axes(shard_axis_names) and returns: self