docstr: Predicts the most similar items to the given item_ids.
  :param item_features: scipy.sparse matrix, tensorflow.data.Dataset, str, or list
  A matrix of item features of shape [n_items, n_item_features].
  If a Dataset, the Dataset must follow the format used in tensorrec.input_utils.
  If a str, the string must be the path to a TFRecord file.
  If a list, the list must contain scipy.sparse matrices, tensorflow.data.Datasets, or strs.
  :param item_ids: list or np.array
  The ids of the items of interest.
  E.g. [4, 8, 12] to get sims for items 4, 8, and 12.
  :param n_similar: int
  The number of similar items to get per item of interest.
  :return: list of lists of tuples
  The first level list corresponds to input arg item_ids.
  The second level list is of length n_similar and contains tuples of (item_id, score) for each similar item.
code: def predict_similar_items(self, item_features, item_ids, n_similar):
      """
      Predicts the most similar items to the given item_ids.
      :param item_features: scipy.sparse matrix, tensorflow.data.Dataset, str, or list
      A matrix of item features of shape [n_items, n_item_features].
      If a Dataset, the Dataset must follow the format used in tensorrec.input_utils.
      If a str, the string must be the path to a TFRecord file.
      If a list, the list must contain scipy.sparse matrices, tensorflow.data.Datasets, or strs.
      :param item_ids: list or np.array
      The ids of the items of interest.
      E.g. [4, 8, 12] to get sims for items 4, 8, and 12.
      :param n_similar: int
      The number of similar items to get per item of interest.
      :return: list of lists of tuples
      The first level list corresponds to input arg item_ids.
      The second level list is of length n_similar and contains tuples of (item_id, score) for each similar item.
      """
  
      # Ensure that the model has been fit
      if self.tf_prediction is None:
          raise ModelNotFitException(method="predict_similar_items")
  
      _, initializers = self._create_datasets_and_initializers(
          interactions=None,
          user_features=None,
          item_features=item_features,
      )
      get_session().run(initializers)
  
      feed_dict = {self.tf_similar_items_ids: np.array(item_ids)}
      sims = self.tf_predict_similar_items.eval(
          session=get_session(), feed_dict=feed_dict
      )
  
      results = []
      for i in range(len(item_ids)):
          item_sims = sims[i]
          best = np.argpartition(item_sims, -n_similar)[-n_similar:]
          item_results = sorted(
              zip(best, item_sims[best]), key=lambda x: -x[1]
          )
          results.append(item_results)
  
      return results
  
imports: from functools import partial
  from itertools import cycle
  import logging
  import numpy
  import os
  import pickle
  from scipy import sparse
  import tensorflow
  from .errors import ModelNotBiasedException, ModelNotFitException, ModelWithoutAttentionException, BatchNonSparseInputException, TfVersionException
  from .input_utils import create_tensorrec_iterator, get_dimensions_from_tensorrec_dataset
  from .loss_graphs import AbstractLossGraph, RMSELossGraph
  from .prediction_graphs import AbstractPredictionGraph, DotProductPredictionGraph
  from .recommendation_graphs import project_biases, split_sparse_tensor_indices, bias_prediction_dense, bias_prediction_serial, rank_predictions, densify_sampled_item_predictions, collapse_mixture_of_tastes, predict_similar_items
  from .representation_graphs import AbstractRepresentationGraph, LinearRepresentationGraph
  from .session_management import get_session
  from .util import sample_items, calculate_batched_alpha, datasets_from_raw_input
label_desc: Use image node_id 12 for calling the TensorRec obj's underlying member method code with example usage: obj.predict_similar_items(item_features, item_ids, n_similar) and returns: results