code: def __init__(
      self,
      model=None,
      embed_size=10,
      sequence_embedding="flatten",
      sequence_embed_size=None,
      hidden_width=1024,
      hidden_layers=4,
      layer_shrink=0.5,
      dropout=0,
      batch_size=32,
      learning_rate=0.001,
      decay=0.0,
      optimizer=None,
      hidden_activation="relu",
      hidden_activity_regularizer=None,
      hidden_bias_regularizer=None,
      hidden_kernel_regularizer=None,
      kernel_initializer="glorot_uniform",
      output_activation=None,
      monitor="val_loss",
      loss=None,
      towers=1,
      cudnn=False,
      multi_gpu_model=True,
      short_names=False,
      batch_norm=False,
  ):
      super(Base, self).__init__()
      if output_activation == "sigmoid" and loss in [
          "mse",
          "mae",
          "mean_squared_error",
          "mean_absolute_error",
      ]:
          logger.warning(
              "Passing output_activation='sigmoid' restricts predictions between 0 and 1. If you have a binary classification problem, you should consider passing loss='binary_crossentropy'. Otherwise you should consider setting output_activation='linear'"
          )
  
      self.towers = towers
      self.embed_size = embed_size
      self.hidden_width = hidden_width
      self.hidden_layers = hidden_layers
      self.layer_shrink = layer_shrink
      self.dropout = dropout
      self.batch_size = batch_size
      self.learning_rate = learning_rate
      self.decay = decay
      self.optimizer = optimizer
      self.hidden_activation = hidden_activation
      self.hidden_activity_regularizer = hidden_activity_regularizer
      self.hidden_bias_regularizer = hidden_bias_regularizer
      self.hidden_kernel_regularizer = hidden_kernel_regularizer
      self.kernel_initializer = kernel_initializer
      self.output_activation = output_activation
      self.monitor = monitor
      self.loss = loss
      self.keras = None
      self.history = None
      self.session = None
      self.model = model
      self.sequence_embedding = sequence_embedding
      self.sequence_embed_size = sequence_embed_size or embed_size
      self.cudnn = cudnn
      self.multi_gpu_model = multi_gpu_model
      self.short_names = short_names
      self.batch_norm = batch_norm
  
imports: from __future__ import absolute_import
  import atexit
  import logging
  import lore.io
  import lore.estimators
  from lore.callbacks import ReloadBest
  from lore.encoders import Continuous, Pass
  from lore.pipelines import Observations
  from lore.util import timed, before_after_callbacks
  from lore.env import require
  import keras
  import keras.backend
  from keras.callbacks import EarlyStopping, TensorBoard, TerminateOnNaN
  from keras.layers import Input, Embedding, Dense, Reshape, Concatenate, Dropout, SimpleRNN, Flatten, LSTM, GRU, BatchNormalization
  from keras.optimizers import Adam
  import numpy
  import pandas
  import tensorflow
  from tensorflow.python.client.timeline import Timeline
  from tensorflow.python.client import device_lib
label_desc: Use image node_id 1 to create a new Base object from inherited base classes: lore.estimators with example: obj = Base(model, embed_size, sequence_embedding, sequence_embed_size, hidden_width, hidden_layers, layer_shrink, dropout, batch_size, learning_rate, decay, optimizer, hidden_activation, hidden_activity_regularizer, hidden_bias_regularizer, hidden_kernel_regularizer, kernel_initializer, output_activation, monitor, loss, towers, cudnn, multi_gpu_model, short_names, batch_norm)