parameters: (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)
return_statement: return self.apply_gradients(grads_and_vars, global_step=global_step,
                                  name=name)
docstring: 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 `compute_gradients()` and `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 colocating 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 some of the variables are not `Variable` objects.
docstring_summary: Add operations to minimize `loss` by updating `var_list`.
docstring_tokens: Add operations to minimize loss by updating var_list .
function: 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):
      """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 `compute_gradients()` and `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 colocating 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 some of the variables are not `Variable` objects.
      """
      grads_and_vars = self.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))
  
      return self.apply_gradients(grads_and_vars, global_step=global_step,
                                  name=name)
function_tokens: 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 ) : grads_and_vars = self . 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 ) ) return self . apply_gradients ( grads_and_vars , global_step = global_step , name = name )