( inputs: strparameters: huggingface_hub.inference._generated.types.audio_classification.AudioClassificationParameters | None = None )

( label: strscore: float )

( function_to_apply: typing.Optional[ForwardRef('AudioClassificationOutputTransform')] = Nonetop_k: int | None = None )

( inputs: typing.Any )

( blob: typing.Anycontent_type: strlabel: str )

( do_sample: bool | None = Noneearly_stopping: typing.Union[bool, ForwardRef('AutomaticSpeechRecognitionEarlyStoppingEnum'), NoneType] = Noneepsilon_cutoff: float | None = Noneeta_cutoff: float | None = Nonemax_length: int | None = Nonemax_new_tokens: int | None = Nonemin_length: int | None = Nonemin_new_tokens: int | None = Nonenum_beam_groups: int | None = Nonenum_beams: int | None = Nonepenalty_alpha: float | None = Nonetemperature: float | None = Nonetop_k: int | None = Nonetop_p: float | None = Nonetypical_p: float | None = Noneuse_cache: bool | None = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.automatic_speech_recognition.AutomaticSpeechRecognitionParameters | None = None )

( text: strchunks: list[huggingface_hub.inference._generated.types.automatic_speech_recognition.AutomaticSpeechRecognitionOutputChunk] | None = None )

( text: strtimestamp: list )

( generation_parameters: huggingface_hub.inference._generated.types.automatic_speech_recognition.AutomaticSpeechRecognitionGenerationParameters | None = Nonereturn_timestamps: bool | None = None )

( messages: listfrequency_penalty: float | None = Nonelogit_bias: list[float] | None = Nonelogprobs: bool | None = Nonemax_tokens: int | None = Nonemodel: str | None = Nonen: int | None = Nonepresence_penalty: float | None = Noneresponse_format: typing.Union[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputResponseFormatText, huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputResponseFormatJSONSchema, huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputResponseFormatJSONObject, NoneType] = Noneseed: int | None = Nonestop: list[str] | None = Nonestream: bool | None = Nonestream_options: huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputStreamOptions | None = Nonetemperature: float | None = Nonetool_choice: typing.Union[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputToolChoiceClass, ForwardRef('ChatCompletionInputToolChoiceEnum'), NoneType] = Nonetool_prompt: str | None = Nonetools: list[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputTool] | None = Nonetop_logprobs: int | None = Nonetop_p: float | None = None )

( name: strparameters: typing.Anydescription: str | None = None )

( name: str )

( name: strdescription: str | None = Noneschema: dict[str, object] | None = Nonestrict: bool | None = None )

( role: strcontent: list[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputMessageChunk] | str | None = Nonename: str | None = Nonetool_calls: list[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputToolCall] | None = None )

( type: ChatCompletionInputMessageChunkTypeimage_url: huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputURL | None = Nonetext: str | None = None )

( type: typing.Literal['json_object'] )

( type: typing.Literal['json_schema']json_schema: ChatCompletionInputJSONSchema )

( type: typing.Literal['text'] )

( include_usage: bool | None = None )

( function: ChatCompletionInputFunctionDefinitiontype: str )

( function: ChatCompletionInputFunctionDefinitionid: strtype: str )

( function: ChatCompletionInputFunctionName )

( url: str )

( choices: listcreated: intid: strmodel: strsystem_fingerprint: strusage: ChatCompletionOutputUsage )

( finish_reason: strindex: intmessage: ChatCompletionOutputMessagelogprobs: huggingface_hub.inference._generated.types.chat_completion.ChatCompletionOutputLogprobs | None = None )

( arguments: strname: strdescription: str | None = None )

( logprob: floattoken: strtop_logprobs: list )

( content: list )

( role: strcontent: str | None = Nonereasoning: str | None = Nonetool_call_id: str | None = Nonetool_calls: list[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionOutputToolCall] | None = None )

( function: ChatCompletionOutputFunctionDefinitionid: strtype: str )

( logprob: floattoken: str )

( completion_tokens: intprompt_tokens: inttotal_tokens: int )

( choices: listcreated: intid: strmodel: strsystem_fingerprint: strusage: huggingface_hub.inference._generated.types.chat_completion.ChatCompletionStreamOutputUsage | None = None )

( delta: ChatCompletionStreamOutputDeltaindex: intfinish_reason: str | None = Nonelogprobs: huggingface_hub.inference._generated.types.chat_completion.ChatCompletionStreamOutputLogprobs | None = None )

( role: strcontent: str | None = Nonereasoning: str | None = Nonetool_call_id: str | None = Nonetool_calls: list[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionStreamOutputDeltaToolCall] | None = None )

( function: ChatCompletionStreamOutputFunctionid: strindex: inttype: str )

( arguments: strname: str | None = None )

( logprob: floattoken: strtop_logprobs: list )

( content: list )

( logprob: floattoken: str )

( completion_tokens: intprompt_tokens: inttotal_tokens: int )

( inputs: typing.Anyparameters: dict[str, typing.Any] | None = None )

( depth: typing.Anypredicted_depth: typing.Any )

( inputs: DocumentQuestionAnsweringInputDataparameters: huggingface_hub.inference._generated.types.document_question_answering.DocumentQuestionAnsweringParameters | None = None )

( image: typing.Anyquestion: str )

( answer: strend: intscore: floatstart: int )

( doc_stride: int | None = Nonehandle_impossible_answer: bool | None = Nonelang: str | None = Nonemax_answer_len: int | None = Nonemax_question_len: int | None = Nonemax_seq_len: int | None = Nonetop_k: int | None = Noneword_boxes: list[list[float] | str] | None = None )

( inputs: list[str] | strnormalize: bool | None = Noneprompt_name: str | None = Nonetruncate: bool | None = Nonetruncation_direction: typing.Optional[ForwardRef('FeatureExtractionInputTruncationDirection')] = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.fill_mask.FillMaskParameters | None = None )

( score: floatsequence: strtoken: inttoken_str: typing.Anyfill_mask_output_token_str: str | None = None )

( targets: list[str] | None = Nonetop_k: int | None = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.image_classification.ImageClassificationParameters | None = None )

( label: strscore: float )

( function_to_apply: typing.Optional[ForwardRef('ImageClassificationOutputTransform')] = Nonetop_k: int | None = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.image_segmentation.ImageSegmentationParameters | None = None )

( label: strmask: strscore: float | None = None )

( mask_threshold: float | None = Noneoverlap_mask_area_threshold: float | None = Nonesubtask: typing.Optional[ForwardRef('ImageSegmentationSubtask')] = Nonethreshold: float | None = None )

( inputs: str | None = Noneparameters: huggingface_hub.inference._generated.types.image_text_to_image.ImageTextToImageParameters | None = None )

( image: typing.Any )

( guidance_scale: float | None = Nonenegative_prompt: str | None = Nonenum_inference_steps: int | None = Noneprompt: str | None = Noneseed: int | None = Nonetarget_size: huggingface_hub.inference._generated.types.image_text_to_image.ImageTextToImageTargetSize | None = None )

( height: intwidth: int )

( inputs: str | None = Noneparameters: huggingface_hub.inference._generated.types.image_text_to_video.ImageTextToVideoParameters | None = None )

( video: typing.Any )

( guidance_scale: float | None = Nonenegative_prompt: str | None = Nonenum_frames: float | None = Nonenum_inference_steps: int | None = Noneprompt: str | None = Noneseed: int | None = Nonetarget_size: huggingface_hub.inference._generated.types.image_text_to_video.ImageTextToVideoTargetSize | None = None )

( height: intwidth: int )

( inputs: strparameters: huggingface_hub.inference._generated.types.image_to_image.ImageToImageParameters | None = None )

( image: typing.Any )

( guidance_scale: float | None = Nonenegative_prompt: str | None = Nonenum_inference_steps: int | None = Noneprompt: str | None = Nonetarget_size: huggingface_hub.inference._generated.types.image_to_image.ImageToImageTargetSize | None = None )

( height: intwidth: int )

( do_sample: bool | None = Noneearly_stopping: typing.Union[bool, ForwardRef('ImageToTextEarlyStoppingEnum'), NoneType] = Noneepsilon_cutoff: float | None = Noneeta_cutoff: float | None = Nonemax_length: int | None = Nonemax_new_tokens: int | None = Nonemin_length: int | None = Nonemin_new_tokens: int | None = Nonenum_beam_groups: int | None = Nonenum_beams: int | None = Nonepenalty_alpha: float | None = Nonetemperature: float | None = Nonetop_k: int | None = Nonetop_p: float | None = Nonetypical_p: float | None = Noneuse_cache: bool | None = None )

( inputs: typing.Anyparameters: huggingface_hub.inference._generated.types.image_to_text.ImageToTextParameters | None = None )

( generated_text: typing.Anyimage_to_text_output_generated_text: str | None = None )

( generation_parameters: huggingface_hub.inference._generated.types.image_to_text.ImageToTextGenerationParameters | None = Nonemax_new_tokens: int | None = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.image_to_video.ImageToVideoParameters | None = None )

( video: typing.Any )

( guidance_scale: float | None = Nonenegative_prompt: str | None = Nonenum_frames: float | None = Nonenum_inference_steps: int | None = Noneprompt: str | None = Noneseed: int | None = Nonetarget_size: huggingface_hub.inference._generated.types.image_to_video.ImageToVideoTargetSize | None = None )

( height: intwidth: int )

( xmax: intxmin: intymax: intymin: int )

( inputs: strparameters: huggingface_hub.inference._generated.types.object_detection.ObjectDetectionParameters | None = None )

( box: ObjectDetectionBoundingBoxlabel: strscore: float )

( threshold: float | None = None )

( inputs: QuestionAnsweringInputDataparameters: huggingface_hub.inference._generated.types.question_answering.QuestionAnsweringParameters | None = None )

( context: strquestion: str )

( answer: strend: intscore: floatstart: int )

( align_to_words: bool | None = Nonedoc_stride: int | None = Nonehandle_impossible_answer: bool | None = Nonemax_answer_len: int | None = Nonemax_question_len: int | None = Nonemax_seq_len: int | None = Nonetop_k: int | None = None )

( inputs: SentenceSimilarityInputDataparameters: dict[str, typing.Any] | None = None )

( sentences: listsource_sentence: str )

( inputs: strparameters: huggingface_hub.inference._generated.types.summarization.SummarizationParameters | None = None )

( summary_text: str )

( clean_up_tokenization_spaces: bool | None = Nonegenerate_parameters: dict[str, typing.Any] | None = Nonetruncation: typing.Optional[ForwardRef('SummarizationTruncationStrategy')] = None )

( inputs: TableQuestionAnsweringInputDataparameters: huggingface_hub.inference._generated.types.table_question_answering.TableQuestionAnsweringParameters | None = None )

( question: strtable: dict )

( answer: strcells: listcoordinates: listaggregator: str | None = None )

( padding: typing.Optional[ForwardRef('Padding')] = Nonesequential: bool | None = Nonetruncation: bool | None = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.text2text_generation.Text2TextGenerationParameters | None = None )

( generated_text: typing.Anytext2_text_generation_output_generated_text: str | None = None )

( clean_up_tokenization_spaces: bool | None = Nonegenerate_parameters: dict[str, typing.Any] | None = Nonetruncation: typing.Optional[ForwardRef('Text2TextGenerationTruncationStrategy')] = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.text_classification.TextClassificationParameters | None = None )

( label: strscore: float )

( function_to_apply: typing.Optional[ForwardRef('TextClassificationOutputTransform')] = Nonetop_k: int | None = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.text_generation.TextGenerationInputGenerateParameters | None = Nonestream: bool | None = None )

( adapter_id: str | None = Nonebest_of: int | None = Nonedecoder_input_details: bool | None = Nonedetails: bool | None = Nonedo_sample: bool | None = Nonefrequency_penalty: float | None = Nonegrammar: huggingface_hub.inference._generated.types.text_generation.TextGenerationInputGrammarType | None = Nonemax_new_tokens: int | None = Nonerepetition_penalty: float | None = Nonereturn_full_text: bool | None = Noneseed: int | None = Nonestop: list[str] | None = Nonetemperature: float | None = Nonetop_k: int | None = Nonetop_n_tokens: int | None = Nonetop_p: float | None = Nonetruncate: int | None = Nonetypical_p: float | None = Nonewatermark: bool | None = None )

( type: TypeEnumvalue: typing.Any )

( generated_text: strdetails: huggingface_hub.inference._generated.types.text_generation.TextGenerationOutputDetails | None = None )

( finish_reason: TextGenerationOutputFinishReasongenerated_text: strgenerated_tokens: intprefill: listtokens: listseed: int | None = Nonetop_tokens: list[list[huggingface_hub.inference._generated.types.text_generation.TextGenerationOutputToken]] | None = None )

( finish_reason: TextGenerationOutputFinishReasongenerated_tokens: intprefill: listtokens: listbest_of_sequences: list[huggingface_hub.inference._generated.types.text_generation.TextGenerationOutputBestOfSequence] | None = Noneseed: int | None = Nonetop_tokens: list[list[huggingface_hub.inference._generated.types.text_generation.TextGenerationOutputToken]] | None = None )

( id: intlogprob: floattext: str )

( id: intlogprob: floatspecial: booltext: str )

( index: inttoken: TextGenerationStreamOutputTokendetails: huggingface_hub.inference._generated.types.text_generation.TextGenerationStreamOutputStreamDetails | None = Nonegenerated_text: str | None = Nonetop_tokens: list[huggingface_hub.inference._generated.types.text_generation.TextGenerationStreamOutputToken] | None = None )

( finish_reason: TextGenerationOutputFinishReasongenerated_tokens: intinput_length: intseed: int | None = None )

( id: intlogprob: floatspecial: booltext: str )

( do_sample: bool | None = Noneearly_stopping: typing.Union[bool, ForwardRef('TextToAudioEarlyStoppingEnum'), NoneType] = Noneepsilon_cutoff: float | None = Noneeta_cutoff: float | None = Nonemax_length: int | None = Nonemax_new_tokens: int | None = Nonemin_length: int | None = Nonemin_new_tokens: int | None = Nonenum_beam_groups: int | None = Nonenum_beams: int | None = Nonepenalty_alpha: float | None = Nonetemperature: float | None = Nonetop_k: int | None = Nonetop_p: float | None = Nonetypical_p: float | None = Noneuse_cache: bool | None = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.text_to_audio.TextToAudioParameters | None = None )

( audio: typing.Anysampling_rate: float )

( generation_parameters: huggingface_hub.inference._generated.types.text_to_audio.TextToAudioGenerationParameters | None = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.text_to_image.TextToImageParameters | None = None )

( image: typing.Any )

( guidance_scale: float | None = Noneheight: int | None = Nonenegative_prompt: str | None = Nonenum_inference_steps: int | None = Nonescheduler: str | None = Noneseed: int | None = Nonewidth: int | None = None )

( do_sample: bool | None = Noneearly_stopping: typing.Union[bool, ForwardRef('TextToSpeechEarlyStoppingEnum'), NoneType] = Noneepsilon_cutoff: float | None = Noneeta_cutoff: float | None = Nonemax_length: int | None = Nonemax_new_tokens: int | None = Nonemin_length: int | None = Nonemin_new_tokens: int | None = Nonenum_beam_groups: int | None = Nonenum_beams: int | None = Nonepenalty_alpha: float | None = Nonetemperature: float | None = Nonetop_k: int | None = Nonetop_p: float | None = Nonetypical_p: float | None = Noneuse_cache: bool | None = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.text_to_speech.TextToSpeechParameters | None = None )

( audio: typing.Anysampling_rate: float | None = None )

( generation_parameters: huggingface_hub.inference._generated.types.text_to_speech.TextToSpeechGenerationParameters | None = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.text_to_video.TextToVideoParameters | None = None )

( video: typing.Any )

( guidance_scale: float | None = Nonenegative_prompt: list[str] | None = Nonenum_frames: float | None = Nonenum_inference_steps: int | None = Noneseed: int | None = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.token_classification.TokenClassificationParameters | None = None )

( end: intscore: floatstart: intword: strentity: str | None = Noneentity_group: str | None = None )

( aggregation_strategy: typing.Optional[ForwardRef('TokenClassificationAggregationStrategy')] = Noneignore_labels: list[str] | None = Nonestride: int | None = None )

( inputs: strparameters: huggingface_hub.inference._generated.types.translation.TranslationParameters | None = None )

( translation_text: str )

( clean_up_tokenization_spaces: bool | None = Nonegenerate_parameters: dict[str, typing.Any] | None = Nonesrc_lang: str | None = Nonetgt_lang: str | None = Nonetruncation: typing.Optional[ForwardRef('TranslationTruncationStrategy')] = None )

( inputs: typing.Anyparameters: huggingface_hub.inference._generated.types.video_classification.VideoClassificationParameters | None = None )

( label: strscore: float )

( frame_sampling_rate: int | None = Nonefunction_to_apply: typing.Optional[ForwardRef('VideoClassificationOutputTransform')] = Nonenum_frames: int | None = Nonetop_k: int | None = None )

( inputs: VisualQuestionAnsweringInputDataparameters: huggingface_hub.inference._generated.types.visual_question_answering.VisualQuestionAnsweringParameters | None = None )

( image: typing.Anyquestion: str )

( score: floatanswer: str | None = None )

( top_k: int | None = None )

( inputs: strparameters: ZeroShotClassificationParameters )

( label: strscore: float )

( candidate_labels: listhypothesis_template: str | None = Nonemulti_label: bool | None = None )

( inputs: strparameters: ZeroShotImageClassificationParameters )

( label: strscore: float )

( candidate_labels: listhypothesis_template: str | None = None )

( xmax: intxmin: intymax: intymin: int )

( inputs: strparameters: ZeroShotObjectDetectionParameters )

( box: ZeroShotObjectDetectionBoundingBoxlabel: strscore: float )

( candidate_labels: list )