docstr: This function will return all the parameters that (might) in `condition`
  It will return a list or tensor args name
  E.g.
  def forward(self, a, b=1, c: int = 3, *args, **kwargs):
      pass
  it will return ['a'] if include=(torch.Tensor)
  
  :param include: tuple of type or "all".
  :param exclude: tuple of type or "all".
  
  Note: "all" means all the types are allowed or disallowed, except those
        stated in the opposite parameter.
  Note: exclude has higher priority if conflict instruction is provided
docstr_tok: ["This","function","will","return","all","the","parameters","that","(","might",")","in","`","condition","`","It","will","return","a","list","or","tensor","args","name","E.g",".","def","forward","(","self",",","a",",","b=1",",","c",":","int","=","3",",","*","args",",","*","*","kwargs",")",":","pass","it","will","return","[","'","a","'","]","if","include=","(","torch.Tensor",")",":","param","include",":","tuple","of","type","or","``","all","''",".",":","param","exclude",":","tuple","of","type","or","``","all","''",".","Note",":","``","all","''","means","all","the","types","are","allowed","or","disallowed",",","except","those","stated","in","the","opposite","parameter",".","Note",":","exclude","has","higher","priority","if","conflict","instruction","is","provided"]
code: def get_conditional_args(
      self,
      include: Optional[Union[tuple, str]] = (
          torch.Tensor,
          torch.FloatTensor,
          torch.LongTensor,
      ),
      exclude: Optional[Union[tuple, str]] = (),
  ):
      """
      This function will return all the parameters that (might) in `condition`
      It will return a list or tensor args name
      E.g.
      def forward(self, a, b=1, c: int = 3, *args, **kwargs):
          pass
      it will return ['a'] if include=(torch.Tensor)
  
      :param include: tuple of type or "all".
      :param exclude: tuple of type or "all".
  
      Note: "all" means all the types are allowed or disallowed, except those
            stated in the opposite parameter.
      Note: exclude has higher priority if conflict instruction is provided
      """
      include_all = True if include == "all" else False
      exclude_all = True if exclude == "all" else False
  
      fitted_args = []
      if self.forward_defaults is None:
          defaults_length = 0
      else:
          defaults_length = len(self.forward_defaults)
      args_length = len(self.forward_args)
      for i, arg in enumerate(self.forward_args):
          # check if type hint is provided
          flag = False
          if arg in self.forward_annotations:
              if (
                  include_all
                  or self.forward_annotations[arg] in include
              ):
                  flag = True
              if (
                  exclude_all
                  or self.forward_annotations[arg] in exclude
              ):
                  flag = False
              if flag:
                  fitted_args.append(arg)
              continue
          # check if defaults is meets requirement
          default_args_start_from = args_length - defaults_length
          if i >= default_args_start_from:
              flag = False
              if (
                  include_all
                  or type(
                      self.forward_defaults[i - default_args_start_from]
                  )
                  in include
              ):
                  flag = True
              if (
                  exclude_all
                  or type(
                      self.forward_defaults[i - default_args_start_from]
                  )
                  in exclude
              ):
                  flag = False
              if flag:
                  fitted_args.append(arg)
              continue
          # nothing is provided, we assume it meets requirement
          fitted_args.append(arg)
      return fitted_args
  
imports: import inspect
  from typing import Optional, Union
  import torch
label_desc: Use image node_id 3 for calling the ModelInfo obj's underlying member method code with example usage: obj.get_conditional_args(include, exclude) and returns: fitted_args