class_docstr: An Agent class for all optimization techniques.
class_docstr_tok: ["An","Agent","class","for","all","optimization","techniques","."]
docstr: Fills the agent's decision variables with static values. Note that this
  method ignore the agent's bounds, so use it carefully.
  
  Args:
      values: Values to be filled.
docstr_tok: ["Fills","the","agent","'s","decision","variables","with","static","values",".","Note","that","this","method","ignore","the","agent","'s","bounds",",","so","use","it","carefully",".","Args",":","values",":","Values","to","be","filled","."]
code: def fill_with_static(self, values: np.ndarray) -> None:
      """Fills the agent's decision variables with static values. Note that this
      method ignore the agent's bounds, so use it carefully.
  
      Args:
          values: Values to be filled.
  
      """
  
      values = np.asarray(values)
      if not values.shape:
          values = np.expand_dims(values, -1)
      if values.shape[0] != self.n_variables:
          raise e.SizeError(
              "`values` should be the same size as `n_variables`"
          )
  
      for j, value in enumerate(values):
          self.position[j] = value
  
imports: import time
  from typing import Dict, List, Optional, Union
  import numpy
  import opytimizer.math.random
  import opytimizer.utils.constant
  import opytimizer.utils.exception
  from opytimizer.utils import logging
label_desc: Use image node_id 21 for calling the Agent obj's underlying member method code with example usage: obj.fill_with_static(values) without return types