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')
def ApplyG(op, dy, _):
  return [None, g(dy)]  # Do not backprop to f(x).
```
  }];

  let arguments = (ins

'<td class="separator">&nbsp;</td><td><a href="#" data-action="toggleMeridian"><span class="'+this.icons.down+'"></span></a></td>':"")+"</tr></table>",this.template){case"modal":a='<div class="bootstrap-timepicker-widget modal hide fade in" data-backdrop="'+(this.modalBackdrop?"true":"false")+'"><div class="modal-header"><a href="#" class="close" data-dismiss="modal">&times;</a><h3>Pick a Time</h3></div><div class="modal-content">'+f+'</div><div class="modal-footer"><a href="#" class="btn btn-primary"...

olumn;flex-direction:column;width:100%;pointer-events:auto;background-color:#fff;background-clip:padding-box;border:1px solid rgba(0,0,0,.2);border-radius:.3rem;outline:0}.modal-backdrop{position:fixed;top:0;left:0;z-index:1040;width:100vw;height:100vh;background-color:#000}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:.5}.modal-header{display:-ms-flexbox;display:flex;-ms-flex-align:start;align-items:flex-start;-ms-flex-pack:justify;justify-content:space-between;padding:1rem 1rem;border-bottom:1px...

olumn;flex-direction:column;width:100%;pointer-events:auto;background-color:#fff;background-clip:padding-box;border:1px solid rgba(0,0,0,.2);border-radius:.3rem;outline:0}.modal-backdrop{position:fixed;top:0;left:0;z-index:1040;width:100vw;height:100vh;background-color:#000}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:.5}.modal-header{display:-ms-flexbox;display:flex;-ms-flex-align:start;align-items:flex-start;-ms-flex-pack:justify;justify-content:space-between;padding:1rem 1rem;border-bottom:1px...

  // CHECK-DAG: %[[SOFTMAX:.*]] = "tf.Div"(%[[SOFTMAX_EXP]], %[[SOFTMAX_SUM]]) : (tensor<2x3xf32>, tensor<2x1xf32>) -> tensor<2x3xf32>

  // CHECK-DAG: %[[BACKPROP:.*]] = "tf.Sub"(%[[SOFTMAX]], %[[LABELS]]) : (tensor<2x3xf32>, tensor<2x3xf32>) -> tensor<2x3xf32>
  // CHECK: return %[[LOSS]], %[[BACKPROP]]

  %0:2 = "tf.SoftmaxCrossEntropyWithLogits"(%features, %labels) : (tensor<2x3xf32>, tensor<2x3xf32>) -> (tensor<2xf32>, tensor<2x3xf32>)

        *   `tf.compat.v1.nn.fused_batch_norm` backprop to `offset` when
            `is_training=False`
        *   `tf.image.adjust_contrast` forward
        *   `tf.nn.depthwise_conv2d` backprop to `filter` when not using cuDNN
            convolution
        *   `tf.image.resize` with `method=ResizeMethod.NEAREST` backprop
        *   `tf.math.bincount` - TODO: confirm exception added

on:column;flex-direction:column;width:100%;pointer-events:auto;background-color:#fff;background-clip:padding-box;border:1px solid rgba(0,0,0,.2);border-radius:.3rem;outline:0}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:.5}.modal-header{display:-webkit-box;display:-ms-flexbox;display:flex;-webkit-box-align:start;-ms-flex-align:start;align-items:flex-start;-webkit-box-pack:justify;-ms-fl...

box-shadow($modal-content-box-shadow-xs);\n  // Remove focus outline from opened modal\n  outline: 0;\n}\n\n// Modal background\n.modal-backdrop {\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: $zindex-modal-backdrop;\n  width: 100vw;\n  height: 100vh;\n  background-color: $modal-backdrop-bg;\n\n  // Fade for backdrop\n  &.fade { opacity: 0; }\n  &.show { opacity: $modal-backdrop-opacity; }\n}\n\n// Modal header\n// Top section of the modal w/ title and dismiss\n.modal-header {\n  display: flex;\n  align-items:...

box-shadow($modal-content-box-shadow-xs);\n  // Remove focus outline from opened modal\n  outline: 0;\n}\n\n// Modal background\n.modal-backdrop {\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: $zindex-modal-backdrop;\n  width: 100vw;\n  height: 100vh;\n  background-color: $modal-backdrop-bg;\n\n  // Fade for backdrop\n  &.fade { opacity: 0; }\n  &.show { opacity: $modal-backdrop-opacity; }\n}\n\n// Modal header\n// Top section of the modal w/ title and dismiss\n.modal-header {\n  display: flex;\n  align-items:...

  let description = [{
    Automatic space to depth transform is done by adding space to depth transform op after host input
    and applying space to depth transform for the first convolution and its backprop filter on TPU.

    For example, original program:

    ```mlir
    module {
      func @while_body {
        %input = "tf.IteratorGetNext"(...) {device = "/CPU:0"}: -> tensor<2x224x224x3xf32>