code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A=None		)      ->  Optional[Any]:
       a					=data
       a					=None
  
  
  
  
  
   def __repr__(	self		)      ->  int:
       a					=''''''
       a					=self
       while temp:
           string_rep += f'''<{temp.data}> ---> '''
           a					=temp.next
       string_rep += "<END>"
       return string_rep
  
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
  
      # if elements_list is empty
      if not elements_list:
          raise Exception('''The Elements List is empty'''	)
  
      # Set first element as Head
      a					=Node(elements_list[0]	)
      a					=head
      # Loop through elements from position 1
      for data in elements_list[1:]:
          a					=Node(lowercase	)
          a					=current.next
      return head
  
  
  lowerCamelCase_     :     Optional[Any]    		=						[1, 3, 5, 3_2, 4_4, 1_2, 4_3]
  print(F'List: {list_data}')
  print("""Creating Linked List from List.""")
  lowerCamelCase_     :     str    		=						make_linked_list(list_data)
  print("""Linked List:""")
  
  
  
  
  
  print(linked_list)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Optional[Any]    		=						{
      """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"vilt"
  
  
   def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
       super().__init__(tie_word_embeddings=__A				,					**__A		)
  
       a					=vocab_size
       a					=type_vocab_size
       a					=modality_type_vocab_size
       a					=max_position_embeddings
  
       a					=hidden_size
       a					=num_hidden_layers
       a					=num_attention_heads
       a					=intermediate_size
       a					=hidden_act
       a					=hidden_dropout_prob
       a					=attention_probs_dropout_prob
       a					=initializer_range
       a					=layer_norm_eps
  
       a					=image_size
       a					=patch_size
       a					=num_channels
       a					=qkv_bias
       a					=max_image_length
       a					=num_images