class_docstr: Named Entity Recognition pipeline using any `ModelForTokenClassification`. See the [named entity recognition
  examples](../task_summary#named-entity-recognition) for more information.
  
  This token recognition pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"ner"` (for predicting the classes of tokens in a sequence: person, organisation, location or miscellaneous).
  
  The models that this pipeline can use are models that have been fine-tuned on a token classification task. See the
  up-to-date list of available models on
  [huggingface.co/models](https://huggingface.co/models?filter=token-classification).
class_docstr_tok: ["Named","Entity","Recognition","pipeline","using","any","`","ModelForTokenClassification","`",".","See","the","[","named","entity","recognition","examples","]","(","..","\/task_summary","#","named-entity-recognition",")","for","more","information",".","This","token","recognition","pipeline","can","currently","be","loaded","from","[","`","pipeline","`","]","using","the","following","task","identifier",":","`","``","ner","''","`","(","for","predicting","the","classes","of","tokens","in","a","sequence",":","person",",","organisation",",","location","or","miscellaneous",")",".","The","models","that","this","pipeline","can","use","are","models","that","have","been","fine-tuned","on","a","token","classification","task",".","See","the","up-to-date","list","of","available","models","on","[","huggingface.co\/models","]","(","https",":","\/\/huggingface.co\/models","?","filter=token-classification",")","."]
docstr: Classify each token of the text(s) given as inputs.
  
  Args:
      inputs (`str` or `List[str]`):
          One or several texts (or one list of texts) for token classification.
  
  Return:
      A list or a list of list of `dict`: Each result comes as a list of dictionaries (one for each token in the
      corresponding input, or each entity if this pipeline was instantiated with an aggregation_strategy) with
      the following keys:
  
      - **word** (`str`) -- The token/word classified. This is obtained by decoding the selected tokens. If you
        want to have the exact string in the original sentence, use `start` and `end`.
      - **score** (`float`) -- The corresponding probability for `entity`.
      - **entity** (`str`) -- The entity predicted for that token/word (it is named *entity_group* when
        *aggregation_strategy* is not `"none"`.
      - **index** (`int`, only present when `aggregation_strategy="none"`) -- The index of the corresponding
        token in the sentence.
      - **start** (`int`, *optional*) -- The index of the start of the corresponding entity in the sentence. Only
        exists if the offsets are available within the tokenizer
      - **end** (`int`, *optional*) -- The index of the end of the corresponding entity in the sentence. Only
        exists if the offsets are available within the tokenizer
docstr_tok: ["Classify","each","token","of","the","text","(","s",")","given","as","inputs",".","Args",":","inputs","(","`","str","`","or","`","List","[","str","]","`",")",":","One","or","several","texts","(","or","one","list","of","texts",")","for","token","classification",".","Return",":","A","list","or","a","list","of","list","of","`","dict","`",":","Each","result","comes","as","a","list","of","dictionaries","(","one","for","each","token","in","the","corresponding","input",",","or","each","entity","if","this","pipeline","was","instantiated","with","an","aggregation_strategy",")","with","the","following","keys",":","-","*","*","word","*","*","(","`","str","`",")","--","The","token\/word","classified",".","This","is","obtained","by","decoding","the","selected","tokens",".","If","you","want","to","have","the","exact","string","in","the","original","sentence",",","use","`","start","`","and","`","end","`",".","-","*","*","score","*","*","(","`","float","`",")","--","The","corresponding","probability","for","`","entity","`",".","-","*","*","entity","*","*","(","`","str","`",")","--","The","entity","predicted","for","that","token\/word","(","it","is","named","*","entity_group","*","when","*","aggregation_strategy","*","is","not","`","``","none","''","`",".","-","*","*","index","*","*","(","`","int","`",",","only","present","when","`","aggregation_strategy=","''","none","''","`",")","--","The","index","of","the","corresponding","token","in","the","sentence",".","-","*","*","start","*","*","(","`","int","`",",","*","optional","*",")","--","The","index","of","the","start","of","the","corresponding","entity","in","the","sentence",".","Only","exists","if","the","offsets","are","available","within","the","tokenizer","-","*","*","end","*","*","(","`","int","`",",","*","optional","*",")","--","The","index","of","the","end","of","the","corresponding","entity","in","the","sentence",".","Only","exists","if","the","offsets","are","available","within","the","tokenizer"]
code: def __call__(self, inputs: Union[str, List[str]], **kwargs):
      """
      Classify each token of the text(s) given as inputs.
  
      Args:
          inputs (`str` or `List[str]`):
              One or several texts (or one list of texts) for token classification.
  
      Return:
          A list or a list of list of `dict`: Each result comes as a list of dictionaries (one for each token in the
          corresponding input, or each entity if this pipeline was instantiated with an aggregation_strategy) with
          the following keys:
  
          - **word** (`str`) -- The token/word classified. This is obtained by decoding the selected tokens. If you
            want to have the exact string in the original sentence, use `start` and `end`.
          - **score** (`float`) -- The corresponding probability for `entity`.
          - **entity** (`str`) -- The entity predicted for that token/word (it is named *entity_group* when
            *aggregation_strategy* is not `"none"`.
          - **index** (`int`, only present when `aggregation_strategy="none"`) -- The index of the corresponding
            token in the sentence.
          - **start** (`int`, *optional*) -- The index of the start of the corresponding entity in the sentence. Only
            exists if the offsets are available within the tokenizer
          - **end** (`int`, *optional*) -- The index of the end of the corresponding entity in the sentence. Only
            exists if the offsets are available within the tokenizer
      """
  
      _inputs, offset_mapping = self._args_parser(inputs, **kwargs)
      if offset_mapping:
          kwargs["offset_mapping"] = offset_mapping
  
      return super().__call__(inputs, **kwargs)
  
imports: import types
  import warnings
  from typing import List, Optional, Tuple, Union
  import numpy
  from ..models.bert.tokenization_bert import BasicTokenizer
  from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
  from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline
label_desc: Use image node_id 3 for calling the TokenClassificationPipeline obj's underlying member method code with example usage: obj.__call__(inputs) and returns: super