easy_rec/python/core/sampler.py [603:637]:
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    neg_features = self._parse_nodes(nodes)
    sparse_nodes = self._hard_neg_sampler.get(src_ids).layer_nodes(1)
    hard_neg_features, hard_neg_indices = self._parse_sparse_nodes(sparse_nodes)

    results = []
    for i, v in enumerate(hard_neg_features):
      if type(v) == list:
        results.append(np.asarray(neg_features[i] + v, order='C', dtype=object))
      else:
        results.append(np.concatenate([neg_features[i], v], axis=0))
    results.append(hard_neg_indices)
    return results

  def get(self, src_ids, dst_ids):
    """Sampling method.

    Args:
      src_ids: user id tensor.
      dst_ids: item id tensor.

    Returns:
      Sampled feature dict. The first batch_size is negative samples, remainder is hard negative samples
    """
    output_types = self._attr_tf_types + [tf.int64]
    output_values = tf.py_func(self._get_impl, [src_ids, dst_ids], output_types)
    result_dict = {}
    for k, t, v in zip(self._attr_names, self._attr_tf_types,
                       output_values[:-1]):
      v.set_shape([None])
      result_dict[k] = v

    hard_neg_indices = output_values[-1]
    hard_neg_indices.set_shape([None, 2])
    result_dict['hard_neg_indices'] = hard_neg_indices
    return result_dict
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easy_rec/python/core/sampler.py [705:739]:
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    neg_features = self._parse_nodes(nodes)
    sparse_nodes = self._hard_neg_sampler.get(src_ids).layer_nodes(1)
    hard_neg_features, hard_neg_indices = self._parse_sparse_nodes(sparse_nodes)

    results = []
    for i, v in enumerate(hard_neg_features):
      if type(v) == list:
        results.append(np.asarray(neg_features[i] + v, order='C', dtype=object))
      else:
        results.append(np.concatenate([neg_features[i], v], axis=0))
    results.append(hard_neg_indices)
    return results

  def get(self, src_ids, dst_ids):
    """Sampling method.

    Args:
      src_ids: user id tensor.
      dst_ids: item id tensor.

    Returns:
      Sampled feature dict. The first batch_size is negative samples, remainder is hard negative samples
    """
    output_types = self._attr_tf_types + [tf.int64]
    output_values = tf.py_func(self._get_impl, [src_ids, dst_ids], output_types)
    result_dict = {}
    for k, t, v in zip(self._attr_names, self._attr_tf_types,
                       output_values[:-1]):
      v.set_shape([None])
      result_dict[k] = v

    hard_neg_indices = output_values[-1]
    hard_neg_indices.set_shape([None, 2])
    result_dict['hard_neg_indices'] = hard_neg_indices
    return result_dict
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