docstr: Method check if the amount of neighbors is too big - clip it
  
  :param input_data: InputData for fit
  :param model_impl: Model to use
docstr_tok: ["Method","check","if","the","amount","of","neighbors","is","too","big","-","clip","it",":","param","input_data",":","InputData","for","fit",":","param","model_impl",":","Model","to","use"]
code: def check_and_correct_k_value(self, input_data, model_impl: Callable):
      """Method check if the amount of neighbors is too big - clip it
  
      :param input_data: InputData for fit
      :param model_impl: Model to use
      """
      current_params = self.model.get_params()
      n_neighbors = current_params.get("n_neighbors")
  
      if n_neighbors > len(input_data.features):
          # Improve the parameter "n_neighbors": n_neighbors <= n_samples
          new_k_value = round(len(input_data.features) / 2)
          if new_k_value == 0:
              new_k_value = 1
          self.params.update(n_neighbors=new_k_value)
          self.model = model_impl(**self.params.to_dict())
  
          prefix = "n_neighbors of K-nn model was changed"
          self.log.info(f"{prefix} from {n_neighbors} to {new_k_value}")
  
imports: from typing import Callable, Optional
  import numpy
  from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
  from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import ModelImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
label_desc: Use image node_id 3 for calling the KNeighborsImplementation obj's underlying member method code with example usage: obj.check_and_correct_k_value(input_data, model_impl) without return types