docstr: Evaluate the inputs against corruptions and return ranks.
  
  Parameters
  ----------
  x: np.array, shape (n,3) or str or GraphDataLoader or
     AbstractGraphPartitioner Data OR Filename of the data file
     OR Data Handle to be used for training.
  batch_size: int
      Batch size to use during training.
      May be overridden if ``x`` is `GraphDataLoader` or
      `AbstractGraphPartitioner` instance
  verbose: bool
      Verbosity mode.
  use_filter: bool or dict
      Whether to use a filter of not. If a dictionary is specified, the
      data in the dict is concatenated and used as filter.
  corrupt_side: str
      Which side to corrupt of a triple to corrupt. It can be the
      subject (``corrupt_size="s"``), the object (``corrupt_size="o"``),
      the subject and the object (``corrupt_size="s+o"`` or
      ``corrupt_size="s,o"``) (default:`"s,o"`).
  entities_subset: list or np.array
      Subset of entities to be used for generating corruptions.
  callbacks: list of keras.callbacks.Callback instances
      List of callbacks to apply during evaluation.
  
  Returns
  -------
  rank: np.array, shape (n, number of corrupted sides)
      Ranking of test triples against subject corruptions and/or
      object corruptions.
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code: def evaluate(
      self,
      x=None,
      batch_size=32,
      verbose=True,
      use_filter=False,
      corrupt_side="s,o",
      entities_subset=None,
      callbacks=None,
  ):
      """
      Evaluate the inputs against corruptions and return ranks.
  
      Parameters
      ----------
      x: np.array, shape (n,3) or str or GraphDataLoader or
         AbstractGraphPartitioner Data OR Filename of the data file
         OR Data Handle to be used for training.
      batch_size: int
          Batch size to use during training.
          May be overridden if ``x`` is `GraphDataLoader` or
          `AbstractGraphPartitioner` instance
      verbose: bool
          Verbosity mode.
      use_filter: bool or dict
          Whether to use a filter of not. If a dictionary is specified, the
          data in the dict is concatenated and used as filter.
      corrupt_side: str
          Which side to corrupt of a triple to corrupt. It can be the
          subject (``corrupt_size="s"``), the object (``corrupt_size="o"``),
          the subject and the object (``corrupt_size="s+o"`` or
          ``corrupt_size="s,o"``) (default:`"s,o"`).
      entities_subset: list or np.array
          Subset of entities to be used for generating corruptions.
      callbacks: list of keras.callbacks.Callback instances
          List of callbacks to apply during evaluation.
  
      Returns
      -------
      rank: np.array, shape (n, number of corrupted sides)
          Ranking of test triples against subject corruptions and/or
          object corruptions.
      """
  
      return self.model.evaluate(
          x,
          batch_size=batch_size,
          verbose=verbose,
          use_filter=use_filter,
          corrupt_side=corrupt_side,
          entities_subset=entities_subset,
          callbacks=callbacks,
      )
  
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 13 for calling the ScoringModelBase obj's underlying member method code with example usage: obj.evaluate(x, batch_size, verbose, use_filter, corrupt_side, entities_subset, callbacks) and returns: self