code: 
  
  import math
  
  
  
  
  def 						A_			(  lowercase_  )			->						bool:
  
  
         if 1 < number < 4:
                # 2 and 3 are primes
                return True
         elif number < 2 or number % 2 == 0 or number % 3 == 0:
                # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
                return False
  
         # All primes number are in format of 6k +/- 1
         for i in range(5      ,		int(math.sqrt(SCREAMING_SNAKE_CASE__  ) + 1  )      ,		6  ):
                if number % i == 0 or number % (i + 2) == 0:
                       return False
         return True
  
  
  
  
  def 						A_			(  lowercase_ = 2000000  )			->						int:
         return sum(num for num in range(3      ,		SCREAMING_SNAKE_CASE__      ,		2  ) if is_prime(SCREAMING_SNAKE_CASE__  )  ) + 2 if n > 2 else 0
  
  
  if __name__ == "__main__":
     print(F"""{solution() = }""")
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from functools import partial
  from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload
  
  import torch
  import torch.nn as nn
  import torch.types
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						torch.Tensor:
         # The first operation in a checkpoint can't be in-place, but it's
         # nice to have in-place addition during inference. Thus...
         if not inplace:
                _snake_case      :							Any     				=						ma + ma
         else:
                ma += ma
  
         return ma
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
         _snake_case      :							str     				=						-1 * len(lowercase_  )
         _snake_case      :							Any     				=						list(range(len(tensor.shape[:zero_index]  )  )  )
         return tensor.permute(first_inds + [zero_index + i for i in inds]  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
         return t.reshape(t.shape[:-no_dims] + (-1,)  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_ = 1E-4  )			->						torch.Tensor:
         _snake_case      :							Any     				=						mask.expand(*value.shape  )
         return torch.sum(mask * value      ,		dim=lowercase_  ) / (eps + torch.sum(lowercase_      ,		dim=lowercase_  ))
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_ = 2.31_25      ,		lowercase_ = 21.68_75      ,		lowercase_ = 64  )			->						torch.Tensor:
         _snake_case      :							int     				=						torch.linspace(lowercase_      ,		lowercase_      ,		no_bins - 1      ,		device=pts.device  )
         _snake_case      :							Optional[Any]     				=						torch.sqrt(torch.sum((pts.unsqueeze(-2  ) - pts.unsqueeze(-3  )) ** 2      ,		dim=-1  )  )
         return torch.bucketize(lowercase_      ,		lowercase_  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						dict:
         _snake_case      :							Dict     				=						dicts[0]
         _snake_case      :							str     				=						{}
         for k, v in first.items():
                _snake_case      :							List[Any]     				=						[d[k] for d in dicts]
                if isinstance(lowercase_      ,		lowercase_  ):
                       _snake_case      :							Any     				=						dict_multimap(lowercase_      ,		lowercase_  )
                else:
                       _snake_case      :							Dict     				=						fn(lowercase_  )
  
         return new_dict
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
         _snake_case      :							str     				=						v_bins.view(((1,) * len(x.shape  )) + (len(lowercase_  ),)  )
         _snake_case      :							Union[str, Any]     				=						x[..., None] - reshaped_bins
         _snake_case      :							Optional[Any]     				=						torch.argmin(torch.abs(lowercase_  )      ,		dim=-1  )
         return nn.functional.one_hot(lowercase_      ,		num_classes=len(lowercase_  )  ).float()
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_ = 0      ,		lowercase_ = 0  )			->						torch.Tensor:
         _snake_case      :							List[Union[slice, torch.Tensor]]     				=						[]
         for i, s in enumerate(data.shape[:no_batch_dims]  ):
                _snake_case      :							Optional[Any]     				=						torch.arange(lowercase_  )
                _snake_case      :							Union[str, Any]     				=						r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape  ) - i - 1)))  )
                ranges.append(lowercase_  )
  
         _snake_case      :							List[Union[slice, torch.Tensor]]     				=						[slice(lowercase_  ) for _ in range(len(data.shape  ) - no_batch_dims  )]
         _snake_case      :							Union[str, Any]     				=						inds
         ranges.extend(lowercase_  )
         # Matt note: Editing this to get around the behaviour of using a list as an array index changing
         # in recent Numpy versions
         return data[tuple(lowercase_  )]
  
  
  lowerCAmelCase_   =   TypeVar("T")
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Dict[Any, Union[dict, list, tuple, Any]]:
         _snake_case      :							Dict[Any, Union[dict, list, tuple, Any]]     				=						{}
         for k, v in dic.items():
                if isinstance(lowercase_      ,		lowercase_  ):
                       _snake_case      :							Dict     				=						dict_map(lowercase_      ,		lowercase_      ,		lowercase_  )
                else:
                       _snake_case      :							List[Any]     				=						tree_map(lowercase_      ,		lowercase_      ,		lowercase_  )
  
         return new_dict
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
         ...
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						dict:
         ...
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						list:
         ...
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						tuple:
         ...
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
         if isinstance(lowercase_      ,		lowercase_  ):
                return dict_map(lowercase_      ,		lowercase_      ,		lowercase_  )
         elif isinstance(lowercase_      ,		lowercase_  ):
                return [tree_map(lowercase_      ,		lowercase_      ,		lowercase_  ) for x in tree]
         elif isinstance(lowercase_      ,		lowercase_  ):
                return tuple(tree_map(lowercase_      ,		lowercase_      ,		lowercase_  ) for x in tree  )
         elif isinstance(lowercase_      ,		lowercase_  ):
                return fn(lowercase_  )
         else:
                print(type(lowercase_  )  )
                raise ValueError('''Not supported'''  )
  lowerCAmelCase_   =   partial(tree_map, leaf_type=torch.Tensor)
  
  
  
  
  
  
  