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 _check_distribution(self, distribution):
      if not isinstance(distribution, dict):
          assert distribution in _DISTRIBUTIONS
      else:
          assert not any(
              i < 0 for i in distribution.values()
          ), "Probabilities cannot be negative"
          assert (
              abs(sum(distribution.values()) - 1) <= 1e-5
          ), "Distribution is not normalized"
          assert self._minval is None
          assert self._maxval is None
  
      return distribution
  
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 4 for calling the FuzzedParameter obj's underlying member method code with example usage: obj._check_distribution(distribution) and returns: distribution