code: def student_t_processes(
      draw,
      kernel_name=None,
      batch_shape=None,
      event_dim=None,
      feature_dim=None,
      feature_ndims=None,
      enable_vars=False,
  ):
      # First draw a kernel.
      k, _ = draw(
          kernel_hps.base_kernels(
              kernel_name=kernel_name,
              batch_shape=batch_shape,
              event_dim=event_dim,
              feature_dim=feature_dim,
              feature_ndims=feature_ndims,
              # Disable variables
              enable_vars=False,
          )
      )
      compatible_batch_shape = draw(
          tfp_hps.broadcast_compatible_shape(k.batch_shape)
      )
      index_points = draw(
          kernel_hps.kernel_input(
              batch_shape=compatible_batch_shape,
              example_ndims=1,
              feature_dim=feature_dim,
              feature_ndims=feature_ndims,
              enable_vars=enable_vars,
              name="index_points",
          )
      )
      params = draw(
          broadcasting_params(
              "StudentTProcess",
              compatible_batch_shape,
              event_dim=event_dim,
              enable_vars=enable_vars,
          )
      )
      stp = student_t_process.StudentTProcess(
          kernel=k,
          index_points=index_points,
          cholesky_fn=lambda x: marginal_fns.retrying_cholesky(x)[0],
          df=params["df"],
          observation_noise_variance=params[
              "observation_noise_variance"
          ],
      )
      return stp
  
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 7 for calling a global function with example usage: student_t_processes(draw, kernel_name, batch_shape, event_dim, feature_dim, feature_ndims, enable_vars) and returns: stp