class_docstr: Basic gradient-descent optimizer.
  
  Base class for other gradient-descent-based optimizers.
  
  A step of the gradient descent optimizer computes the new values via the rule
  
  .. math::
  
      x^{(t+1)} = x^{(t)} - \eta \nabla f(x^{(t)}).
  
  where :math:`\eta` is a user-defined hyperparameter corresponding to step size.
  
  Args:
      stepsize (float): the user-defined hyperparameter :math:`\eta`
  
  .. note::
  
      When using ``torch``, ``tensorflow`` or ``jax`` interfaces, refer to :doc:`Gradients and training </introduction/interfaces>` for suitable optimizers.
code: def __init__(self, stepsize=0.01):
      self.stepsize = stepsize
  
imports: from pennylane._grad import grad
label_desc: Use image node_id 1 to create a new GradientDescentOptimizer object  with example: obj = GradientDescentOptimizer(stepsize)