code: def _pytree_unflatten(aux_data, children):
      keys, metadata = aux_data
      non_tensor_params = metadata["non_tensor_params"]
      non_tensor_params["_check_marginal_cholesky_fn"] = False
      parameters = dict(
          list(zip(keys, children)),
          **non_tensor_params,
          **metadata["callable_params"],
      )
      return GaussianProcess(**parameters)
  
imports: import functools
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import cholesky_util
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import mvn_linear_operator
  from tensorflow_probability.python.distributions.internal import stochastic_process_util
  from tensorflow_probability.python.internal import auto_composite_tensor
  from tensorflow_probability.python.internal import batch_shape_lib
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math import linalg
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import nest
label_desc: Use image node_id 7 for calling a global function with example usage: _pytree_unflatten(aux_data, children) and returns: GaussianProcess