class_docstr: Base class for dynamic hyperparameters.
  
  Args:
      unit ("timesteps" | "episodes" | "updates"): Unit of parameter schedule
          (<span style="color:#00C000"><b>default</b></span>: timesteps).
      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>.
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code: def __init__(
      self,
      *,
      unit="timesteps",
      name=None,
      dtype=None,
      shape=(),
      min_value=None,
      max_value=None,
  ):
      super().__init__(name=name)
  
      assert unit in (None, "timesteps", "episodes", "updates")
      self.unit = unit
  
      self.spec = TensorSpec(
          type=dtype,
          shape=shape,
          min_value=min_value,
          max_value=max_value,
      )
  
      assert (
          self.min_value() is None
          or self.max_value() is None
          or self.min_value() <= self.max_value()
      )
      if self.spec.min_value is not None:
          if self.min_value() is None:
              raise TensorforceError.value(
                  name=self.name,
                  argument="lower bound",
                  value=self.min_value(),
                  hint=("not >= {}".format(self.spec.min_value)),
              )
          elif self.min_value() < self.spec.min_value:
              raise TensorforceError.value(
                  name=self.name,
                  argument="lower bound",
                  value=self.min_value(),
                  hint=("< {}".format(self.spec.min_value)),
              )
      if self.spec.max_value is not None:
          if self.max_value() is None:
              raise TensorforceError.value(
                  name=self.name,
                  argument="upper bound",
                  value=self.max_value(),
                  hint=("not <= {}".format(self.spec.max_value)),
              )
          elif self.max_value() > self.spec.max_value:
              raise TensorforceError.value(
                  name=self.name,
                  argument="upper bound",
                  value=self.max_value(),
                  hint=("> {}".format(self.spec.max_value)),
              )
  
file_path: tensorforce/tensorforce/core/parameters/parameter.py
imports: import tensorflow
  from tensorforce import TensorforceError
  from tensorforce.core import Module, SignatureDict, TensorSpec, tf_function, tf_util
label_desc: Use image node_id 1 to create a new Parameter object from inherited base classes: Module with example: obj = Parameter()