class_docstr: Initialize a new Inference Client.
  
  [`InferenceClient`] aims to provide a unified experience to perform inference. The client can be used
  seamlessly with either the (free) Inference API or self-hosted Inference Endpoints.
  
  Args:
      model (`str`, `optional`):
          The model to run inference with. Can be a model id hosted on the Hugging Face Hub, e.g. `bigcode/starcoder`
          or a URL to a deployed Inference Endpoint. Defaults to None, in which case a recommended model is
          automatically selected for the task.
      token (`str`, *optional*):
          Hugging Face token. Will default to the locally saved token. Pass `token=False` if you don't want to send
          your token to the server.
      timeout (`float`, `optional`):
          The maximum number of seconds to wait for a response from the server. Loading a new model in Inference
          API can take up to several minutes. Defaults to None, meaning it will loop until the server is available.
      headers (`Dict[str, str]`, `optional`):
          Additional headers to send to the server. By default only the authorization and user-agent headers are sent.
          Values in this dictionary will override the default values.
      cookies (`Dict[str, str]`, `optional`):
          Additional cookies to send to the server.
docstr: Compute the semantic similarity between a sentence and a list of other sentences by comparing their embeddings.
  
  Args:
      sentence (`str`):
          The main sentence to compare to others.
      other_sentences (`List[str]`):
          The list of sentences to compare to.
      model (`str`, *optional*):
          The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to
          a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used.
          Defaults to None.
  
  Returns:
      `List[float]`: The embedding representing the input text.
  
  Raises:
      [`InferenceTimeoutError`]:
          If the model is unavailable or the request times out.
      `HTTPError`:
          If the request fails with an HTTP error status code other than HTTP 503.
  
  Example:
  ```py
  >>> from huggingface_hub import InferenceClient
  >>> client = InferenceClient()
  >>> client.sentence_similarity(
  ...     "Machine learning is so easy.",
  ...     other_sentences=[
  ...         "Deep learning is so straightforward.",
  ...         "This is so difficult, like rocket science.",
  ...         "I can't believe how much I struggled with this.",
  ...     ],
  ... )
  [0.7785726189613342, 0.45876261591911316, 0.2906220555305481]
  ```
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code: def sentence_similarity(
      self,
      sentence: str,
      other_sentences: List[str],
      *,
      model: Optional[str] = None,
  ) -> List[float]:
      """
      Compute the semantic similarity between a sentence and a list of other sentences by comparing their embeddings.
  
      Args:
          sentence (`str`):
              The main sentence to compare to others.
          other_sentences (`List[str]`):
              The list of sentences to compare to.
          model (`str`, *optional*):
              The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to
              a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used.
              Defaults to None.
  
      Returns:
          `List[float]`: The embedding representing the input text.
  
      Raises:
          [`InferenceTimeoutError`]:
              If the model is unavailable or the request times out.
          `HTTPError`:
              If the request fails with an HTTP error status code other than HTTP 503.
  
      Example:
      ```py
      >>> from huggingface_hub import InferenceClient
      >>> client = InferenceClient()
      >>> client.sentence_similarity(
      ...     "Machine learning is so easy.",
      ...     other_sentences=[
      ...         "Deep learning is so straightforward.",
      ...         "This is so difficult, like rocket science.",
      ...         "I can't believe how much I struggled with this.",
      ...     ],
      ... )
      [0.7785726189613342, 0.45876261591911316, 0.2906220555305481]
      ```
      """
      response = self.post(
          json={
              "inputs": {
                  "source_sentence": sentence,
                  "sentences": other_sentences,
              }
          },
          model=model,
          task="sentence-similarity",
      )
      return _bytes_to_list(response)
  
imports: import logging
  import time
  import warnings
  from dataclasses import asdict
  from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Literal, Optional, Union, overload
  from requests import HTTPError
  from requests.structures import CaseInsensitiveDict
  from huggingface_hub.constants import ALL_INFERENCE_API_FRAMEWORKS, INFERENCE_ENDPOINT, MAIN_INFERENCE_API_FRAMEWORKS
  from huggingface_hub.inference._common import TASKS_EXPECTING_IMAGES, ContentT, InferenceTimeoutError, ModelStatus, _b64_encode, _b64_to_image, _bytes_to_dict, _bytes_to_image, _bytes_to_list, _fetch_recommended_models, _import_numpy, _is_tgi_server, _open_as_binary, _set_as_non_tgi, _stream_text_generation_response
  from huggingface_hub.inference._text_generation import TextGenerationParameters, TextGenerationRequest, TextGenerationResponse, TextGenerationStreamResponse, raise_text_generation_error
  from huggingface_hub.inference._types import ClassificationOutput, ConversationalOutput, FillMaskOutput, ImageSegmentationOutput, ObjectDetectionOutput, QuestionAnsweringOutput, TableQuestionAnsweringOutput, TokenClassificationOutput
  from huggingface_hub.utils import BadRequestError, build_hf_headers, get_session, hf_raise_for_status
label_desc: Use image node_id 20 for calling the InferenceClient obj's underlying member method code with example usage: obj.sentence_similarity(sentence, other_sentences) and returns: _bytes_to_list