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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def minimize(self, loss, global_step=None, var_list=None, gate_gradients=GATE_OP, aggregation_method=None, colocate_gradients_with_ops=False, name=None, grad_loss=None):
 """Adapted from TensorFlow Optimizer base class member function. Add operations to minimize `loss` by updating `var_list`. This method simply combines calls `compute_gradients()` and `apply_gradients()`. If you want to process the gradient before applying them call `tf.gradients()` and `self.apply_gradients()` explicitly instead of using this function. Args: loss: A Tensor containing the value to minimize. global_step: Optional Variable to increment by one after the variables have been updated. var_list: Optional list or tuple of Variable objects to update to minimize loss. Defaults to the list of variables collected in the graph under the key GraphKeys.TRAINABLE_VARIABLES. gate_gradients: How to gate the computation of gradients. Can be GATE_NONE, GATE_OP, or GATE_GRAPH. aggregation_method: Specifies the method used to combine gradient terms. Valid values are defined in the class AggregationMethod. colocate_gradients_with_ops: If True, try collocating gradients with the corresponding op. name: Optional name for the returned operation. grad_loss: Optional. A Tensor holding the gradient computed for loss. Returns: An Operation that updates the variables in var_list. If global_step was not None, that operation also increments global_step. Raises: ValueError: if no gradients are provided for any variable. """

    grads_and_vars = self._momentum_optimizer.compute_gradients(
        loss,
        var_list=var_list,
        gate_gradients=gate_gradients,
        aggregation_method=aggregation_method,
        colocate_gradients_with_ops=colocate_gradients_with_ops,
        grad_loss=grad_loss)

    vars_with_grad = [v for g, v in grads_and_vars if g is not None]
    if not vars_with_grad:
      raise ValueError(
          "No gradients provided for any variable, check your graph for ops"
          " that do not support gradients, between variables %s and loss %s." %
          ([str(v) for _, v in grads_and_vars], loss))
    for g, v in grads_and_vars:
      print("g ", g)
      print("v ", v)

    return self.apply_gradients(grads_and_vars,
                                global_step=global_step,
                                name=name)