code: def __init__(
      self,
      shape,
      mlp_dims,
      ids,
      init="normal",
      mlp_pretrain=True,
      l2_reg=None,
      train=True,
      scope=None,
      reuse=False,
  ):
      if type(init) == str:
          if init == "normal":
              self._initializer = tf.truncated_normal_initializer(
                  mean=0.0, stddev=0.01, dtype=tf.float32
              )
          elif init == "zero":
              self._initializer = tf.constant_initializer(
                  value=0.0, dtype=tf.float32
              )
      else:
          self._initializer = tf.constant_initializer(
              value=init, dtype=tf.float32
          )
  
      self._shape = shape
      self._ids = ids
      self._mlp_dims = mlp_dims
      self._mlp_pretrain = mlp_pretrain
  
      super(TemporalLatentFactor, self).__init__(
          train=train, l2_reg=l2_reg, scope=scope, reuse=reuse
      )
  
file_path: openrec/openrec/tf1/legacy/modules/extractions/temporal_latent_factor.py
imports: import tensorflow
  import numpy
  from tqdm import tqdm
  from openrec.tf1.legacy.modules.extractions import Extraction
  from openrec.tf1.legacy.modules.extractions import MultiLayerFC
label_desc: Use image node_id 1 to create a new TemporalLatentFactor object from inherited base classes: Extraction with example: obj = TemporalLatentFactor(shape, mlp_dims, ids, init, mlp_pretrain, l2_reg, train, scope, reuse)