{"Regs386.BP", Field, 0},
		{"Regs386.BX", Field, 0},
		{"Regs386.CS", Field, 0},
		{"Regs386.CX", Field, 0},
		{"Regs386.DI", Field, 0},
		{"Regs386.DS", Field, 0},
		{"Regs386.DX", Field, 0},
		{"Regs386.ES", Field, 0},
		{"Regs386.FLAGS", Field, 0},
		{"Regs386.FS", Field, 0},
		{"Regs386.GS", Field, 0},
		{"Regs386.IP", Field, 0},
		{"Regs386.SI", Field, 0},

  }];

  let description = [{
tensors.

This op can be used to override the gradient for complicated functions. For
example, suppose y = f(x) and we wish to apply a custom function g for backprop
such that dx = g(dy). In Python,

```python
with tf.get_default_graph().gradient_override_map(
    {'IdentityN': 'OverrideGradientWithG'}):
  y, _ = identity_n([f(x), x])

@tf.RegisterGradient('OverrideGradientWithG')