class_docstr: FuzzingHelper makes handling FuzzedDataProvider easier with TensorFlow Python fuzzing.
class_docstr_tok: ["FuzzingHelper","makes","handling","FuzzedDataProvider","easier","with","TensorFlow","Python","fuzzing","."]
docstr: Consume a string with given constraints based on a consumed bool.
  
  Args:
    byte_count: Byte count that defaults to _MAX_INT.
  
  Returns:
    Consumed string based on input bytes and constraints.
docstr_tok: ["Consume","a","string","with","given","constraints","based","on","a","consumed","bool",".","Args",":","byte_count",":","Byte","count","that","defaults","to","_MAX_INT",".","Returns",":","Consumed","string","based","on","input","bytes","and","constraints","."]
code: def get_string(self, byte_count=_MAX_INT):
      """Consume a string with given constraints based on a consumed bool.
  
      Args:
        byte_count: Byte count that defaults to _MAX_INT.
  
      Returns:
        Consumed string based on input bytes and constraints.
      """
      return self.fdp.ConsumeString(byte_count)
  
file_path: tensorflow-upstream/tensorflow/security/fuzzing/python_fuzzing.py
imports: import atheris
  import tensorflow
label_desc: Use image node_id 9 for calling the FuzzingHelper obj's underlying member method code with example usage: obj.get_string(byte_count) and returns: self