docstr: Generate MLP model.
  
  Args:
     input_vector_dimension (int): word emb vec length
  
  Returns:
code: def generate_model(self, input_vector_dimension):
      """Generate MLP model.
  
      Args:
         input_vector_dimension (int): word emb vec length
  
      Returns:
      """
      mlp_model = Sequential()
  
      mlp_model.add(
          Dense(
              128,
              activation=self.activation_1,
              input_dim=input_vector_dimension,
          )
      )
      mlp_model.add(Dropout(0.5))
      mlp_model.add(Dense(64, activation=self.activation_2))
      mlp_model.add(Dropout(0.5))
      mlp_model.add(Dense(1, activation=self.activation_3))
      mlp_model.compile(
          metrics=["accuracy"], loss=self.loss, optimizer=self.optimizer
      )
  
      return mlp_model
  
imports: import csv
  import pickle
  import numpy
  import tensorflow
  from os import PathLike
  from pathlib import Path
  from nlp_architect.models.absa.utils import _read_generic_lex_for_similarity
  from nlp_architect.models.absa import TRAIN_OUT, TRAIN_LEXICONS, GENERIC_OP_LEX, LEXICONS_OUT
  from scipy.spatial.distance import cosine
  from sklearn.model_selection import StratifiedKFold
  from tensorflow.keras.layers import Dense, Dropout
  from tensorflow.keras.models import Sequential, load_model
  from nlp_architect.utils.embedding import load_word_embeddings
label_desc: Use image node_id 13 for calling the RerankTerms obj's underlying member method code with example usage: obj.generate_model(input_vector_dimension) and returns: mlp_model