docstr: Get the initializers among tf.keras.initializers.
docstr_tok: ["Get","the","initializers","among","tf.keras.initializers","."]
code: def _get_initializer(self, initializer, initializer_params):
      """Get the initializers among tf.keras.initializers."""
      if initializer == "xavier":
          if initializer_params["uniform"]:
              return tf.keras.initializers.GlorotUniform(seed=self.seed)
          else:
              return tf.keras.initializers.GlorotNormal(seed=self.seed)
      elif initializer == "uniform":
          return tf.keras.initializers.RandomUniform(
              minval=initializer_params.get("low", -0.05),
              maxval=initializer_params.get("high", 0.05),
              seed=self.seed,
          )
      elif initializer == "normal":
          return tf.keras.initializers.RandomNormal(
              mean=initializer_params.get("mean", 0.0),
              stddev=initializer_params.get("std", 0.05),
              seed=self.seed,
          )
      elif initializer == "constant":
          entity_init = initializer_params.get("entity", None)
          rel_init = initializer_params.get("relation", None)
          assert (
              entity_init is not None
          ), "Please pass the `entity` initializer value"
          assert (
              rel_init is not None
          ), "Please pass the `relation` initializer value"
          return [
              tf.constant_initializer(entity_init),
              tf.constant_initializer(rel_init),
          ]
      else:
          return tf.keras.initializers.get(initializer)
  
imports: import numpy
  import tensorflow
  from ampligraph.latent_features.loss_functions import get
  from ampligraph.latent_features.models import ScoringBasedEmbeddingModel
  from ampligraph.latent_features.optimizers import get
  from ampligraph.latent_features.regularizers import get
label_desc: Use image node_id 4 for calling the ScoringModelBase obj's underlying member method code with example usage: obj._get_initializer(initializer, initializer_params) and returns: tf, tf, tf, tf, list, tf