code: def stochastic_processes(
      draw,
      process_name=None,
      kernel_name=None,
      batch_shape=None,
      event_dim=None,
      feature_dim=None,
      feature_ndims=None,
      enable_vars=False,
  ):
      if process_name is None:
          process_name = draw(
              hps.sampled_from(
                  sorted(PARAM_EVENT_NDIMS_BY_PROCESS_NAME.keys())
              )
          )
      if batch_shape is None:
          batch_shape = draw(tfp_hps.shapes())
      if event_dim is None:
          event_dim = draw(hps.integers(min_value=2, max_value=4))
      if feature_dim is None:
          feature_dim = draw(hps.integers(min_value=2, max_value=4))
      if feature_ndims is None:
          feature_ndims = draw(hps.integers(min_value=0, max_value=3))
  
      if process_name == "GaussianProcess":
          return draw(
              gaussian_processes(
                  kernel_name=kernel_name,
                  batch_shape=batch_shape,
                  event_dim=event_dim,
                  feature_dim=feature_dim,
                  feature_ndims=feature_ndims,
                  enable_vars=enable_vars,
              )
          )
      elif process_name == "GaussianProcessRegressionModel":
          return draw(
              gaussian_process_regression_models(
                  kernel_name=kernel_name,
                  batch_shape=batch_shape,
                  event_dim=event_dim,
                  feature_dim=feature_dim,
                  feature_ndims=feature_ndims,
                  enable_vars=enable_vars,
              )
          )
      elif process_name == "VariationalGaussianProcess":
          return draw(
              variational_gaussian_processes(
                  kernel_name=kernel_name,
                  batch_shape=batch_shape,
                  event_dim=event_dim,
                  feature_dim=feature_dim,
                  feature_ndims=feature_ndims,
                  enable_vars=enable_vars,
              )
          )
      elif process_name == "StudentTProcess":
          return draw(
              student_t_processes(
                  kernel_name=kernel_name,
                  batch_shape=batch_shape,
                  event_dim=event_dim,
                  feature_dim=feature_dim,
                  feature_ndims=feature_ndims,
                  enable_vars=enable_vars,
              )
          )
      elif process_name == "StudentTProcessRegressionModel":
          return draw(
              student_t_process_regression_models(
                  kernel_name=kernel_name,
                  batch_shape=batch_shape,
                  event_dim=event_dim,
                  feature_dim=feature_dim,
                  feature_ndims=feature_ndims,
                  enable_vars=enable_vars,
              )
          )
      raise ValueError(
          'Stochastic process "{}" not found.'.format(process_name)
      )
  
imports: from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import numpy
  import six
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gaussian_process
  from tensorflow_probability.python.distributions import gaussian_process_regression_model
  from tensorflow_probability.python.distributions import student_t_process
  from tensorflow_probability.python.distributions import student_t_process_regression_model
  from tensorflow_probability.python.distributions import variational_gaussian_process
  from tensorflow_probability.python.experimental.distributions import marginal_fns
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import hypothesis_testlib
label_desc: Use image node_id 3 for calling a global function with example usage: stochastic_processes(draw, process_name, kernel_name, batch_shape, event_dim, feature_dim, feature_ndims, enable_vars) and returns: draw, draw, draw, draw, draw