class_docstr: Specification for a parameter to be generated during fuzzing.
class_docstr_tok: ["Specification","for","a","parameter","to","be","generated","during","fuzzing","."]
code: def _loguniform(self, state):
      output = int(
          2
          ** state.uniform(
              low=np.log2(self._minval)
              if self._minval is not None
              else None,
              high=np.log2(self._maxval)
              if self._maxval is not None
              else None,
          )
      )
      if self._minval is not None and output < self._minval:
          return self._minval
      if self._maxval is not None and output > self._maxval:
          return self._maxval
      return output
  
imports: import functools
  import itertools
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
label_desc: Use image node_id 5 for calling the FuzzedParameter obj's underlying member method code with example usage: obj._loguniform(state) and returns: output, self, self