text: import tensorflow as tf
  import tensorflow.keras.layers as layers
  
  
  def Actor_Continuos_Default(state_dimension, action_dimension, action_bound):
  
      inputs = layers.Input((state_dimension, ))
      dense_1 = layers.Dense(32, activation='relu')(inputs)
      dense_2 = layers.Dense(32, activation='relu')(dense_1)
      out_mu = layers.Dense(action_dimension, activation='tanh')(dense_2)
      mu_output = layers.Lambda(lambda x: x * action_bound)(out_mu)
      std_output = layers.Dense(action_dimension, activation='softplus')(dense_2)
      return tf.keras.models.Model(inputs, [mu_output, std_output])
  
  
  def Actor_Continuos_IMG(state_dimension, action_dimension, action_bound):
  
      inputs = layers.Input((state_dimension, ))
      cnn_1 = layers.Conv2D(64, 10, activation='relu')(inputs)
      batch_norm = layers.BatchNormalization()(cnn_1)
      dropout = layers.Dropout(0.2)(batch_norm)
      cnn_2 = layers.Conv2D(128, 8, activation='relu')(dropout)
      batch_norm = layers.BatchNormalization()(cnn_2)
      dropout = layers.Dropout(0.2)(batch_norm)
      flatten = layers.Flatten()(dropout)
      out_mu = layers.Dense(action_dimension, activation='relu')(flatten)
      mu_output = layers.Lambda(lambda x: x * action_bound)(out_mu)
      std_output = layers.Dense(action_dimension, activation='softmax')(flatten)
      return tf.keras.models.Model(inputs, [mu_output, std_output])
  
  
  def Critic_Default(state_dimension):
      return tf.keras.Sequential([
          layers.Input((state_dimension, )),
          layers.Dense(32, activation='relu'),
          layers.Dense(32, activation='relu'),
          layers.Dense(16, activation='relu'),
          layers.Dense(1, activation='linear')
      ])
  
  
  def Critic_CNN(state_dimension):
      return tf.keras.Sequential([
          layers.Input((state_dimension,)),
          layers.Conv2D(64, 10, activation='relu'),
          layers.Dropout(0.2),
          layers.Conv2D(128, 8, activation='relu'),
          layers.Dropout(0.2),
          layers.Flatten(),
          layers.Dense(50, activation='relu'),
          layers.Dense(1, activations='linear')
      ])