class_docstr: Time varying squared exponential kernel.
  For more info see the TV-GP-UCB paper:
  http://proceedings.mlr.press/v51/bogunovic16.pdf
class_docstr_tok: ["Time","varying","squared","exponential","kernel",".","For","more","info","see","the","TV-GP-UCB","paper",":","http",":","\/\/proceedings.mlr.press\/v51\/bogunovic16.pdf"]
code: def __init__(
      self,
      input_dim,
      variance=1.0,
      lengthscale=1.0,
      epsilon=0.0,
      active_dims=None,
  ):
      import_optional.ensure()
      super().__init__(input_dim, active_dims, "time_se")
      self.variance = Param("variance", variance)
      self.lengthscale = Param("lengthscale", lengthscale)
      self.epsilon = Param("epsilon", epsilon)
      self.link_parameters(
          self.variance, self.lengthscale, self.epsilon
      )
  
file_path: orion/src/orion/algo/pbt/pb2_utils.py
imports: from copy import deepcopy
  import numpy
  from scipy.optimize import minimize
  from sklearn.metrics import euclidean_distances, pairwise_distances
  from orion.core.utils.module_import import ImportOptional
label_desc: Use image node_id 1 to create a new TVSquaredExp object from inherited base classes: Kern with example: obj = TVSquaredExp(input_dim, variance, lengthscale, epsilon, active_dims)