code: 
  
  
  
  
  
  from __future__ import annotations
  
  
  
  
  
  
  class      UpperCAmelCase			:
   def __init__(       self   :Optional[int]		, lowercase_   :Dict							)->							Dict:
       A__       =    data
       A__       =    None
  
  
  
  
  
  
  class      UpperCAmelCase			:
   def __init__(       self   :Tuple							)->							Dict:
       A__       =    None
   def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :str							)->							List[str]:
       A__       =    Node(__UpperCamelCase							)
       A__       =    self.head
       A__       =    new_node
       return self.head.data
  
  
  
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Tuple:
       A__       =    self.head
       A__       =    self.head
       if self.head:
           while fast_pointer and fast_pointer.next:
               A__       =    fast_pointer.next.next
               A__       =    slow_pointer.next
           return slow_pointer.data
       else:
           print("No element found."							)
           return None
  
  
  if __name__ == "__main__":
    __lowerCAmelCase							:			str				=LinkedList()
    for _ in range(int(input().strip())):
      __lowerCAmelCase							:			Optional[int]				=int(input().strip())
      link.push(data)
    print(link.middle_element())
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowerCAmelCase							:			int				={
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Any				=["LayoutLMTokenizerFast"]
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Dict				=[
        "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LayoutLMForMaskedLM",
        "LayoutLMForSequenceClassification",
        "LayoutLMForTokenClassification",
        "LayoutLMForQuestionAnswering",
        "LayoutLMModel",
        "LayoutLMPreTrainedModel",
    ]
  
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			str				=[
        "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFLayoutLMForMaskedLM",
        "TFLayoutLMForSequenceClassification",
        "TFLayoutLMForTokenClassification",
        "TFLayoutLMForQuestionAnswering",
        "TFLayoutLMMainLayer",
        "TFLayoutLMModel",
        "TFLayoutLMPreTrainedModel",
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer
  
    try:
      if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
    try:
      if not is_tf_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )
  
  else:
    import sys
  
    __lowerCAmelCase							:			int				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  