class_docstr: Class wrapping around the ScoringBasedEmbeddingModel with the TransE
  scoring function.
class_docstr_tok: ["Class","wrapping","around","the","ScoringBasedEmbeddingModel","with","the","TransE","scoring","function","."]
docstr: Initialize the ScoringBasedEmbeddingModel with the TransE
  scoring function.
docstr_tok: ["Initialize","the","ScoringBasedEmbeddingModel","with","the","TransE","scoring","function","."]
code: def __init__(
      self,
      k=100,
      eta=2,
      epochs=100,
      batches_count=100,
      seed=0,
      embedding_model_params={
          "corrupt_sides": ["s,o"],
          "negative_corruption_entities": "all",
          "norm": 1,
          "normalize_ent_emb": False,
      },
      optimizer="adam",
      optimizer_params={"lr": 0.0005},
      loss="nll",
      loss_params={},
      regularizer=None,
      regularizer_params={},
      initializer="xavier",
      initializer_params={"uniform": False},
      verbose=False,
      model=None,
  ):
      """Initialize the ScoringBasedEmbeddingModel with the TransE
      scoring function."""
      super().__init__(
          k,
          eta,
          epochs,
          batches_count,
          seed,
          embedding_model_params,
          optimizer,
          optimizer_params,
          loss,
          loss_params,
          regularizer,
          regularizer_params,
          initializer,
          initializer_params,
          verbose,
          model,
      )
  
      self.model_name = "TransE"
  
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 1 to create a new TransE object from inherited base classes: ScoringModelBase with example: obj = TransE(k, eta, epochs, batches_count, seed, embedding_model_params, optimizer, optimizer_params, loss, loss_params, regularizer, regularizer_params, initializer, initializer_params, verbose, model)