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: Classifying a target category (a group) based on a set of attributes.
  
  Args:
      table (`Dict[str, Any]`):
          Set of attributes to classify.
      model (`str`):
          The model to use for the tabular-classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
          a deployed Inference Endpoint.
  
  Returns:
      `List`: a list of labels, one per row in the initial table.
  
  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()
  >>> table = {
  ...     "fixed_acidity": ["7.4", "7.8", "10.3"],
  ...     "volatile_acidity": ["0.7", "0.88", "0.32"],
  ...     "citric_acid": ["0", "0", "0.45"],
  ...     "residual_sugar": ["1.9", "2.6", "6.4"],
  ...     "chlorides": ["0.076", "0.098", "0.073"],
  ...     "free_sulfur_dioxide": ["11", "25", "5"],
  ...     "total_sulfur_dioxide": ["34", "67", "13"],
  ...     "density": ["0.9978", "0.9968", "0.9976"],
  ...     "pH": ["3.51", "3.2", "3.23"],
  ...     "sulphates": ["0.56", "0.68", "0.82"],
  ...     "alcohol": ["9.4", "9.8", "12.6"],
  ... }
  >>> client.tabular_classification(table=table, model="julien-c/wine-quality")
  ["5", "5", "5"]
  ```
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code: def tabular_classification(
      self, table: Dict[str, Any], *, model: str
  ) -> List[str]:
      """
      Classifying a target category (a group) based on a set of attributes.
  
      Args:
          table (`Dict[str, Any]`):
              Set of attributes to classify.
          model (`str`):
              The model to use for the tabular-classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to
              a deployed Inference Endpoint.
  
      Returns:
          `List`: a list of labels, one per row in the initial table.
  
      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()
      >>> table = {
      ...     "fixed_acidity": ["7.4", "7.8", "10.3"],
      ...     "volatile_acidity": ["0.7", "0.88", "0.32"],
      ...     "citric_acid": ["0", "0", "0.45"],
      ...     "residual_sugar": ["1.9", "2.6", "6.4"],
      ...     "chlorides": ["0.076", "0.098", "0.073"],
      ...     "free_sulfur_dioxide": ["11", "25", "5"],
      ...     "total_sulfur_dioxide": ["34", "67", "13"],
      ...     "density": ["0.9978", "0.9968", "0.9976"],
      ...     "pH": ["3.51", "3.2", "3.23"],
      ...     "sulphates": ["0.56", "0.68", "0.82"],
      ...     "alcohol": ["9.4", "9.8", "12.6"],
      ... }
      >>> client.tabular_classification(table=table, model="julien-c/wine-quality")
      ["5", "5", "5"]
      ```
      """
      response = self.post(
          json={"table": table},
          model=model,
          task="tabular-classification",
      )
      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 23 for calling the InferenceClient obj's underlying member method code with example usage: obj.tabular_classification(table) and returns: _bytes_to_list