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: Compute embeddings for inputs.
  
  Args:
      inputs: Sequence of inputs to embed.
  
  Returns:
      Embeddings for each input in `inputs`.
docstr_tok: ["Compute","embeddings","for","inputs",".","Args",":","inputs",":","Sequence","of","inputs","to","embed",".","Returns",":","Embeddings","for","each","input","in","`","inputs","`","."]
code: def embed(
      self,
      inputs: Sequence[Any],
  ) -> BatchEmbeddings:
      """Compute embeddings for inputs.
  
      Args:
          inputs: Sequence of inputs to embed.
  
      Returns:
          Embeddings for each input in `inputs`.
      """
      self._lazy_init()
      embeddings = []
      for b_offset in range(0, len(inputs), self.batch_size):
          batch = inputs[b_offset : b_offset + self.batch_size]
          embs = self.embedder.embed(inputs=batch)
          embeddings.append(embs)
  
      embeddings = np.concatenate(embeddings, axis=0)
      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 3 for calling the UniSim obj's underlying member method code with example usage: obj.embed(inputs) and returns: embeddings