class_docstr: Manages hyperparameter optimisation using random search for a single GPU.
  
  Attributes:
    param_ranges: Discrete hyperparameter range for random search.
    results: Dataframe of validation results.
    fixed_params: Fixed model parameters per experiment.
    saved_params: Dataframe of parameters trained.
    best_score: Minimum validation loss observed thus far.
    optimal_name: Key to best configuration.
    hyperparam_folder: Where to save optimisation outputs.
class_docstr_tok: ["Manages","hyperparameter","optimisation","using","random","search","for","a","single","GPU",".","Attributes",":","param_ranges",":","Discrete","hyperparameter","range","for","random","search",".","results",":","Dataframe","of","validation","results",".","fixed_params",":","Fixed","model","parameters","per","experiment",".","saved_params",":","Dataframe","of","parameters","trained",".","best_score",":","Minimum","validation","loss","observed","thus","far",".","optimal_name",":","Key","to","best","configuration",".","hyperparam_folder",":","Where","to","save","optimisation","outputs","."]
docstr: Returns previously saved parameters given a key.
docstr_tok: ["Returns","previously","saved","parameters","given","a","key","."]
code: def _get_params_from_name(self, name):
      """Returns previously saved parameters given a key."""
      params = self.saved_params
  
      selected_params = dict(params[name])
  
      if self._override_w_fixed_params:
          for k in self.fixed_params:
              selected_params[k] = self.fixed_params[k]
  
      return selected_params
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import os
  import shutil
  import libs.utils
  import numpy
  import pandas
label_desc: Use image node_id 3 for calling the HyperparamOptManager obj's underlying member method code with example usage: obj._get_params_from_name(name) and returns: selected_params