class_docstr: A simple MLP that can either be used independently or put on top
  of pretrained models (such as BERT) and act as a classifier.
  Args:
      hidden_size (int): the size of each layer
      num_classes (int): number of output classes
      num_layers (int): number of layers
      activation (str): type of activations for layers in between
      log_softmax (bool): whether to add a log_softmax layer before output
class_docstr_tok: ["A","simple","MLP","that","can","either","be","used","independently","or","put","on","top","of","pretrained","models","(","such","as","BERT",")","and","act","as","a","classifier",".","Args",":","hidden_size","(","int",")",":","the","size","of","each","layer","num_classes","(","int",")",":","number","of","output","classes","num_layers","(","int",")",":","number","of","layers","activation","(","str",")",":","type","of","activations","for","layers","in","between","log_softmax","(","bool",")",":","whether","to","add","a","log_softmax","layer","before","output"]
code: def last_linear_layer(self):
      return getattr(self, f"layer{self.layers - 1}")
  
imports: import torch
label_desc: Use image node_id 2 for calling the MultiLayerPerceptron obj's underlying member method code with example usage: obj.last_linear_layer() and returns: getattr