class_docstr: An exception thrown when not all entities/relations are covered by triples.
class_docstr_tok: ["An","exception","thrown","when","not","all","entities\/relations","are","covered","by","triples","."]
docstr: Initialize the error.
  
  :param arr: shape: (num_indices,)
      the array of covering triple IDs
  :param name:
      the name to use for creating the error message
docstr_tok: ["Initialize","the","error",".",":","param","arr",":","shape",":","(","num_indices",",",")","the","array","of","covering","triple","IDs",":","param","name",":","the","name","to","use","for","creating","the","error","message"]
code: def __init__(self, arr, name: str = "ids"):
      """
      Initialize the error.
  
      :param arr: shape: (num_indices,)
          the array of covering triple IDs
      :param name:
          the name to use for creating the error message
      """
      r = sorted((arr < 0).nonzero(as_tuple=False))
      super().__init__(
          f"Could not cover the following {name} from the provided triples: {r}. One possible reason is that you are"
          f" working with triples from a non-compact ID mapping, i.e. where the IDs do not span the full range of "
          f"[0, ..., num_ids - 1]",
      )
  
imports: import logging
  import typing
  from abc import abstractmethod
  from typing import Collection, Optional, Sequence, Set, Tuple, Type, Union
  import numpy
  import pandas
  import torch
  from class_resolver.api import ClassResolver, HintOrType
  from ..constants import COLUMN_LABELS
  from ..typing import LABEL_HEAD, LABEL_RELATION, LABEL_TAIL, MappedTriples, Target, TorchRandomHint
  from ..utils import ensure_torch_random_state
label_desc: Use image node_id 1 to create a new TripleCoverageError object from inherited base classes: RuntimeError with example: obj = TripleCoverageError(arr, name)