code: 
  
  
  
  
  
  
  """simple docstring"""
  from sklearn.datasets import load_iris
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import KNeighborsClassifier
  
  # Load iris file
  snake_case   :   Optional[int]    		=		load_iris()
  iris.keys()
  
  
  print(f"""Target names: \n {iris.target_names} """)
  print(f"""\n Features: \n {iris.feature_names}""")
  
  # Train set e Test set
  snake_case		, snake_case		, snake_case		, snake_case   :   List[str]    		=		train_test_split(
      iris["""data"""], iris["""target"""], random_state=4
  )
  
  # KNN
  
  snake_case   :   Union[str, Any]    		=		KNeighborsClassifier(n_neighbors=1)
  knn.fit(X_train, y_train)
  
  # new array to test
  snake_case   :   Optional[int]    		=		[[1, 2, 1, 4], [2, 3, 4, 5]]
  
  snake_case   :   str    		=		knn.predict(X_new)
  
  print(
      f"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
      f""" {iris['target_names'][prediction]}"""
  )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  snake_case   :   str    		=		{"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Union[str, Any]    		=		[
       """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SEWDForCTC""",
       """SEWDForSequenceClassification""",
       """SEWDModel""",
       """SEWDPreTrainedModel""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_sew_d import (
        SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWDForCTC,
        SEWDForSequenceClassification,
        SEWDModel,
        SEWDPreTrainedModel,
    )
  
  
  else:
   import sys
  
   snake_case   :   Union[str, Any]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)