class_docstr: Abstract base class for UniSim.
  
  UniSim is designed to find near-duplicates and similar matches for content
  (such as text, images, multi-modal inputs) using embedding models. The
  UniSim packages enables finding the similarity between two items, indexing
  and querying for similar items, and finding near-duplicates in a dataset.
docstr: Display UniSim info.
docstr_tok: ["Display","UniSim","info","."]
code: def info(self):
      """Display UniSim info."""
      self._lazy_init()
  
      print("[Embedder]")
      print(f"|-batch_size: {self.batch_size}")
      print(f"|-model_id: {self.model_id}")
      print(f"|-embedding_size: {self.embedding_size}")
  
      print("[Indexer]")
      print(f"|-index_type: {self.index_type.name}")
      print(f"|-use_accelerator: {self.use_accelerator}")
      print(f"|-store index data: {self.store_data}")
      print(f"|-return embeddings: {self.return_embeddings}")
  
imports: from abc import ABC
  from typing import Any, Dict, List, Sequence
  import numpy
  from pandas import DataFrame
  from .None import backend
  from .config import get_accelerator
  from .dataclass import ResultCollection
  from .embedder import Embedder
  from .enums import AcceleratorType, IndexerType
  from .indexer import Indexer
  from .types import BatchEmbeddings
label_desc: Use image node_id 9 for calling the UniSim obj's underlying member method code with example usage: obj.info() without return types