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: loc_lin_layer weights are the weight of hidden layers after the first fully connected layer.
  
  Returns:
      loc_lin_layer weights
docstr_tok: ["loc_lin_layer","weights","are","the","weight","of","hidden","layers","after","the","first","fully","connected","layer",".","Returns",":","loc_lin_layer","weights"]
code: def loc_lin_layer_weights(self) -> torch.Tensor:
      """
      loc_lin_layer weights are the weight of hidden layers after the first fully connected layer.
  
      Returns:
          loc_lin_layer weights
      """
      return self._loc_lin_layer_weights
  
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 5 for calling the NotearsMLP obj's underlying member method code with example usage: obj.loc_lin_layer_weights() and returns: self