ents(),t._resetScrollbar(),g(t._element).trigger(le.HIDDEN)})},t._removeBackdrop=function(){this._backdrop&&(g(this._backdrop).remove(),this._backdrop=null)},t._showBackdrop=function(t){var e=this,n=g(this._element).hasClass(de)?de:"";if(this._isShown&&this._config.backdrop){if(this._backdrop=document.createElement("div"),this._backdrop.className=ue,n&&this._backdrop.classList.add(n),g(this._backdrop).appendTo(document.body),g(this._element).on(le.CLICK_DISMISS,function(t){e._ignoreBackdropClick...

ents(),t._resetScrollbar(),g(t._element).trigger(le.HIDDEN)})},t._removeBackdrop=function(){this._backdrop&&(g(this._backdrop).remove(),this._backdrop=null)},t._showBackdrop=function(t){var e=this,n=g(this._element).hasClass(de)?de:"";if(this._isShown&&this._config.backdrop){if(this._backdrop=document.createElement("div"),this._backdrop.className=ue,n&&this._backdrop.classList.add(n),g(this._backdrop).appendTo(document.body),g(this._element).on(le.CLICK_DISMISS,function(t){e._ignoreBackdropClick...

  // 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>)

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>