class_docstr: Converts text to embeddings using the RETSim text embedding model.
  
  The RETSim model embeds texts into 256-dimensional float vectors. The embeddings
  are trained using TensorFlow Similarity to be robust against adversarial attacks.
  The model is able to handle inputs of arbitrary length.
docstr: Returns the embedding size of the text embedding model.
code: def embedding_size(self) -> int:
      """Returns the embedding size of the text embedding model."""
      return self._embedding_size
  
file_path: unisim/unisim/embedder/text/text_embedder.py
imports: from typing import Sequence
  import numpy
  from ...types import BatchEmbeddings
  from ..embedder import Embedder
  from .binarizer import binarizer
label_desc: Use image node_id 2 for calling the TextEmbedder obj's underlying member method code with example usage: obj.embedding_size() and returns: self