code: 
  
  
  
  
  
  import abc
  import copy
  import dataclasses
  from dataclasses import dataclass
  from typing import ClassVar, Dict, Type, TypeVar
  
  from ..features import Features
  
  
  lowerCAmelCase_   =   TypeVar("T", bound="TaskTemplate")
  
  @dataclass(frozen=UpperCamelCase_				)
  class      A    (abc.ABC				):
        # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
        _SCREAMING_SNAKE_CASE					=							42
        _SCREAMING_SNAKE_CASE					=							42
        _SCREAMING_SNAKE_CASE					=							42
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   T:
  
  
  
               '''simple docstring'''
  
               return copy.deepcopy(self					)
  
  
  
        @property
        def    __a (					self					)      ->   Features:
  
  
  
               '''simple docstring'''
  
               return Features(**self.input_schema     ,  **self.label_schema					)
  
  
  
        @property
        @abc.abstractmethod
        def    __a (					self					)      ->   Dict[str, str]:
  
  
  
               '''simple docstring'''
  
               raise NotImplementedError
  
  
  
        @classmethod
        def    __a (					cls     ,  lowercase_					)      ->   T:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							int     				=						{f.name for f in dataclasses.fields(cls					)}
               return cls(**{k: v for k, v in template_dict.items() if k in field_names}					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import numpy as np
  
  
  
  
  def 						A_			(  lowercase_  )			->						Optional[Any]:
         _snake_case      :							Union[str, Any]     				=						np.exp(lowercase_  )
  
         # Add up the all the exponentials
         _snake_case      :							List[str]     				=						np.sum(lowercase_  )
  
         # Divide every exponent by the sum of all exponents
         _snake_case      :							Optional[int]     				=						exponent_vector / sum_of_exponents
  
         return softmax_vector
  
  
  if __name__ == "__main__":
     print(softmax((0,)))
  
  
  
  
  
  
  