class_docstr: Random hyperparameter (specification key: `random`).
  
  Args:
      distribution ("normal" | "uniform"): Distribution type for random hyperparameter value
          (<span style="color:#C00000"><b>required</b></span>).
      kwargs: Additional arguments dependent on distribution type.<br>
          Normal distribution:
          <ul>
          <li><b>mean</b> (<i>float</i>) &ndash; Mean
          (<span style="color:#00C000"><b>default</b></span>: 0.0).</li>
          <li><b>stddev</b> (<i>float > 0.0</i>) &ndash; Standard deviation
          (<span style="color:#00C000"><b>default</b></span>: 1.0).</li>
          </ul>
          Uniform distribution:
          <ul>
          <li><b>minval</b> (<i>int / float</i>) &ndash; Lower bound
          (<span style="color:#00C000"><b>default</b></span>: 0 / 0.0).</li>
          <li><b>maxval</b> (<i>float > minval</i>) &ndash; Upper bound
          (<span style="color:#00C000"><b>default</b></span>: 1.0 for float,
          <span style="color:#C00000"><b>required</b></span> for int).</li>
          </ul>
      name (string): <span style="color:#0000C0"><b>internal use</b></span>.
      dtype (type): <span style="color:#0000C0"><b>internal use</b></span>.
      shape (iter[int > 0]): <span style="color:#0000C0"><b>internal use</b></span>.
      min_value (dtype-compatible value): <span style="color:#0000C0"><b>internal use</b></span>.
      max_value (dtype-compatible value): <span style="color:#0000C0"><b>internal use</b></span>.
code: def min_value(self):
      if self.distribution == "uniform":
          return self.spec.py_type()(self.kwargs.get("minval", 0))
  
      else:
          return super().min_value()
  
file_path: tensorforce/tensorforce/core/parameters/random.py
imports: import tensorflow
  from tensorforce.core.parameters import Parameter
label_desc: Use image node_id 2 for calling the Random obj's underlying member method code with example usage: obj.min_value() and returns: self, super