code: def total_norm_constraint(
      tensor_vars, max_norm, epsilon=1e-7, return_norm=False
  ):
      """Rescales a list of tensors based on their combined norm
  
      If the combined norm of the input tensors exceeds the threshold then all
      tensors are rescaled such that the combined norm is equal to the threshold.
  
      Scaling the norms of the gradients is often used when training recurrent
      neural networks [1]_.
  
      Parameters
      ----------
      tensor_vars: List of TensorVariables.
          Tensors to be rescaled.
      max_norm: float
          Threshold value for total norm.
      epsilon: scalar, optional
          Value used to prevent numerical instability when dividing by
          very small or zero norms.
      return_norm: bool
          If true the total norm is also returned.
  
      Returns
      -------
      tensor_vars_scaled: list of TensorVariables
          The scaled tensor variables.
      norm: PyTensor scalar
          The combined norms of the input variables prior to rescaling,
          only returned if ``return_norms=True``.
  
      Examples
      --------
      >>> from lasagne.layers import InputLayer, DenseLayer
      >>> import lasagne
      >>> from lasagne.updates import sgd, total_norm_constraint
      >>> x = pt.matrix()
      >>> y = pt.ivector()
      >>> l_in = InputLayer((5, 10))
      >>> l1 = DenseLayer(l_in, num_units=7, nonlinearity=pt.special.softmax)
      >>> output = lasagne.layers.get_output(l1, x)
      >>> cost = pt.mean(pt.nnet.categorical_crossentropy(output, y))
      >>> all_params = lasagne.layers.get_all_params(l1)
      >>> all_grads = pt.grad(cost, all_params)
      >>> scaled_grads = total_norm_constraint(all_grads, 5)
      >>> updates = sgd(scaled_grads, all_params, learning_rate=0.1)
  
      Notes
      -----
      The total norm can be used to monitor training.
  
      References
      ----------
      .. [1] Sutskever, I., Vinyals, O., & Le, Q. V. (2014): Sequence to sequence
         learning with neural networks. In Advances in Neural Information
         Processing Systems (pp. 3104-3112).
      """
      norm = pt.sqrt(sum(pt.sum(tensor**2) for tensor in tensor_vars))
      dtype = np.dtype(pytensor.config.floatX).type
      target_norm = pt.clip(norm, 0, dtype(max_norm))
      multiplier = target_norm / (dtype(epsilon) + norm)
      tensor_vars_scaled = [step * multiplier for step in tensor_vars]
  
      if return_norm:
          return tensor_vars_scaled, norm
      else:
          return tensor_vars_scaled
  
imports: from collections import OrderedDict
  from functools import partial
  import numpy
  import pytensor
  import pytensor.tensor
  import pymc
label_desc: Use image node_id 15 for calling a global function with example usage: total_norm_constraint(tensor_vars, max_norm, epsilon, return_norm) and returns: tensor_vars_scaled,  norm, tensor_vars_scaled