class_docstr: Class for NOTEARS MLP (Multi-layer Perceptron) model.
  The model weights consist of dag_layer and loc_lin_layer weights respectively.
  dag_layer weight is the weight of the first fully connected layer which determines the causal structure.
  loc_lin_layer weights are the weight of hidden layers after the first fully connected layer
class_docstr_tok: ["Class","for","NOTEARS","MLP","(","Multi-layer","Perceptron",")","model",".","The","model","weights","consist","of","dag_layer","and","loc_lin_layer","weights","respectively",".","dag_layer","weight","is","the","weight","of","the","first","fully","connected","layer","which","determines","the","causal","structure",".","loc_lin_layer","weights","are","the","weight","of","hidden","layers","after","the","first","fully","connected","layer"]
docstr: Get the vector of feature biases
  
  Returns:
      bias vector if use_bias is True, otherwise None
docstr_tok: ["Get","the","vector","of","feature","biases","Returns",":","bias","vector","if","use_bias","is","True",",","otherwise","None"]
code: def bias(self) -> Union[np.ndarray, None]:
      """
      Get the vector of feature biases
  
      Returns:
          bias vector if use_bias is True, otherwise None
      """
      bias = self.dag_layer_bias
      return bias if bias is None else bias.cpu().detach().numpy()
  
imports: import logging
  from typing import Iterable, List, Tuple, Union
  import numpy
  import scipy.optimize
  import torch
  from sklearn.base import BaseEstimator
  from torch import nn
  from causalnex.structure.pytorch.dist_type._base import DistTypeBase
  from causalnex.structure.pytorch.nonlinear import LocallyConnected
label_desc: Use image node_id 8 for calling the NotearsMLP obj's underlying member method code with example usage: obj.bias() and returns: unknown