code: def sgd(loss_or_grads, params, learning_rate):
      """Stochastic Gradient Descent (SGD) updates
  
      Generates update expressions of the form:
  
      * ``param := param - learning_rate * gradient``
  
      Parameters
      ----------
      loss_or_grads : symbolic expression or list of expressions
          A scalar loss expression, or a list of gradient expressions
      params : list of shared variables
          The variables to generate update expressions for
      learning_rate : float or symbolic scalar
          The learning rate controlling the size of update steps
  
      Returns
      -------
      OrderedDict
          A dictionary mapping each parameter to its update expression
      """
      grads = get_or_compute_grads(loss_or_grads, params)
      updates = OrderedDict()
  
      for param, grad in zip(params, grads):
          updates[param] = param - learning_rate * grad
  
      return updates
  
file_path: Lasagne/lasagne/updates.py
imports: from collections import OrderedDict
  import numpy
  import theano
  import theano.tensor
  from .None import utils
label_desc: Use image node_id 2 for calling a global function with example usage: sgd(loss_or_grads, params, learning_rate) and returns: updates