class_docstr: A linear value function (baseline) based on features.
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      reg_coeff (float): Regularization coefficient.
      features (list[str]): Name of features.
      name (str): Name of baseline.
class_docstr_tok: ["A","linear","value","function","(","baseline",")","based","on","features",".","Args",":","env_spec","(","garage.envs.env_spec.EnvSpec",")",":","Environment","specification",".","reg_coeff","(","float",")",":","Regularization","coefficient",".","features","(","list","[","str","]",")",":","Name","of","features",".","name","(","str",")",":","Name","of","baseline","."]
docstr: Extract features from path.
  
  Args:
      path (list[dict]): Sample paths.
  
  Returns:
      numpy.ndarray: Extracted features.
docstr_tok: ["Extract","features","from","path",".","Args",":","path","(","list","[","dict","]",")",":","Sample","paths",".","Returns",":","numpy.ndarray",":","Extracted","features","."]
code: def _features(self, path):
      """Extract features from path.
  
      Args:
          path (list[dict]): Sample paths.
  
      Returns:
          numpy.ndarray: Extracted features.
  
      """
      features = [
          np.clip(path[feature_name], -10, 10)
          for feature_name in self._feature_names
      ]
      n = len(path["observations"])
      return np.concatenate(
          sum([[f, f**2] for f in features], []) + [np.ones((n, 1))],
          axis=1,
      )
  
imports: import numpy
  from garage.np.baselines import LinearFeatureBaseline
label_desc: Use image node_id 2 for calling the LinearMultiFeatureBaseline obj's underlying member method code with example usage: obj._features(path) and returns: np