class_docstr: Fits a GP by tuning the kernel hyper-params. 
class_docstr_tok: ["Fits","a","GP","by","tuning","the","kernel","hyper-params","."]
docstr: Set up for the SE kernel. 
code: def _se_kernel_set_up(self):
      """Set up for the SE kernel."""
      # Scale
      self.scale_log_bounds = [
          np.log(0.1 * self.Y_var),
          np.log(10 * self.Y_var),
      ]
      self.param_order.append(["scale", "cts"])
      # Bandwidths
      X_std_norm = np.linalg.norm(self.X, "fro") + 1e-4
      single_bandwidth_log_bounds = [
          np.log(0.01 * X_std_norm),
          np.log(10 * X_std_norm),
      ]
      if self.options.use_same_bandwidth:
          self.bandwidth_log_bounds = [single_bandwidth_log_bounds]
          self.param_order.append(["same_dim_bandwidths", "cts"])
      else:
          self.bandwidth_log_bounds = [
              single_bandwidth_log_bounds
          ] * self.dim
          for _ in range(self.dim):
              self.param_order.append(["dim_bandwidths", "cts"])
      self.cts_hp_bounds += [
          self.scale_log_bounds
      ] + self.bandwidth_log_bounds
  
imports: from __future__ import division
  from argparse import Namespace
  import numpy
  from .None import gp_core, mf_gp
  from .None import kernel
  from ..utils.ancillary_utils import get_list_of_floats_as_str
  from ..utils.general_utils import get_sublist_from_indices, map_to_bounds
  from ..utils.option_handler import get_option_specs, load_options
  from ..utils.oper_utils import random_sample_from_discrete_domain
  from ..utils.reporters import get_reporter
label_desc: Use image node_id 3 for calling the EuclideanGPFitter obj's underlying member method code with example usage: obj._se_kernel_set_up() without return types