class_docstr: Torch implementation of CLIP from https://github.com/openai/CLIP.
  
  Args:
      config: a :class:`TorchCLIPModelConfig`
docstr: Generates an embedding for the given text prompts.
  
  Args:
      prompts: an iterable of text strings
  
  Returns:
      a ``num_prompts x num_dims`` array of prompt embeddings
docstr_tok: ["Generates","an","embedding","for","the","given","text","prompts",".","Args",":","prompts",":","an","iterable","of","text","strings","Returns",":","a","``","num_prompts","x","num_dims","``","array","of","prompt","embeddings"]
code: def embed_prompts(self, prompts):
      """Generates an embedding for the given text prompts.
  
      Args:
          prompts: an iterable of text strings
  
      Returns:
          a ``num_prompts x num_dims`` array of prompt embeddings
      """
      return self._embed_prompts(prompts).detach().cpu().numpy()
  
imports: import logging
  import os
  from packaging.version import Version
  import warnings
  import eta.core.web
  import fiftyone
  import fiftyone.core.models
  import fiftyone.core.utils
  import fiftyone.utils.torch
  import fiftyone.zoo.models
  import torch
  from .tokenizer import SimpleTokenizer
  from .model import build_model
label_desc: Use image node_id 4 for calling the TorchCLIPModel obj's underlying member method code with example usage: obj.embed_prompts(prompts) and returns: self