code: def broadcasting_params(
      draw, kernel_name, batch_shape, event_dim=None, enable_vars=False
  ):
      """Draws a dict of parameters which should yield the given batch shape."""
      if kernel_name not in INSTANTIABLE_BASE_KERNELS:
          raise ValueError("Unknown Kernel name {}".format(kernel_name))
      params_event_ndims = INSTANTIABLE_BASE_KERNELS[
          kernel_name
      ].params_event_ndims
  
      def _constraint(param):
          return constraint_for(kernel_name, param)
  
      return draw(
          tfp_hps.broadcasting_params(
              batch_shape,
              params_event_ndims,
              event_dim=event_dim,
              enable_vars=enable_vars,
              constraint_fn_for=_constraint,
              mutex_params=MUTEX_PARAMS,
              dtype=np.float64,
          )
      )
  
imports: import collections
  import contextlib
  import inspect
  import logging
  import re
  import hypothesis
  from hypothesis.extra import numpy
  import hypothesis.strategies
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.distributions import marginal_fns
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.math import psd_kernels
label_desc: Use image node_id 4 for calling a global function with example usage: broadcasting_params(draw, kernel_name, batch_shape, event_dim, enable_vars) and returns: draw, constraint_for