class_docstr: TextSim class for computing textual similarity.
  
  TextSim leverages the RETSim embedding model to compute the syntactic
  similarity between texts, retrieve similar documents, and deduplicate
  datasets. The model is a highly-optimized, small model that runs
  efficiently on both GPU and CPU, which makes TextSim significantly faster
  than traditional edit distance-based techniques and other neural-based
  embeddings.
  
  For more details on the RETSim model, please see TODO (marinazh) [paper link].
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docstr: Compute embeddings for input texts.
  
  Args:
      inputs: Sequence of input texts to embed.
  
  Returns:
      Embeddings for each text in `inputs`. The RETSim model embeds
      each input text into a 256-dimensional float32 array.
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code: def embed(self, inputs: Sequence[str]) -> BatchEmbeddings:
      """Compute embeddings for input texts.
  
      Args:
          inputs: Sequence of input texts to embed.
  
      Returns:
          Embeddings for each text in `inputs`. The RETSim model embeds
          each input text into a 256-dimensional float32 array.
      """
      return super().embed(inputs=inputs)
  
file_path: unisim/unisim/textsim.py
imports: from typing import Any, Dict, List, Sequence
  from pandas import DataFrame
  from tabulate import tabulate
  from .dataclass import Result, ResultCollection
  from .embedder import TextEmbedder
  from .enums import IndexerType
  from .types import BatchEmbeddings
  from .unisim import UniSim
label_desc: Use image node_id 4 for calling the TextSim obj's underlying member method code with example usage: obj.embed(inputs) and returns: super