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: Initialize a TextEmbedder.
  
  Args:
      batch_size: Batch size for inference.
  
      model_id: ID of model, defaults to `text/retsim/v1`.
  
      verbose: Verbosity mode.
code: def __init__(
      self,
      batch_size: int,
      model_id: str = "text/retsim/v1",
      verbose: int = 0,
  ):
      """Initialize a TextEmbedder.
  
      Args:
          batch_size: Batch size for inference.
  
          model_id: ID of model, defaults to `text/retsim/v1`.
  
          verbose: Verbosity mode.
      """
      # model loading is handled in the super
      super().__init__(
          batch_size=batch_size, model_id=model_id, verbose=verbose
      )
      self._chunk_size = 512
      self._embedding_size = 256
  
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 1 to create a new TextEmbedder object from inherited base classes: Embedder with example: obj = TextEmbedder(batch_size, model_id, verbose)