return result;
}

// The backprop loop counter and main backprop loop run in their own execution
// frame (conceptually, the main forward loop and forward loop counter run
// together in a frame, then the backprop loop counter and backprop loop run
// together in a different frame). This returns the frame name to use for the
// backprop while loops.

  DCHECK(while_ctx != nullptr);

  // Record 'summed_grads' as the backprop input associated with 'exit_node'
  std::map<Node*, Output>& backprops = while_backprops_[while_ctx];
  DCHECK(backprops.find(exit_node) == backprops.end());
  backprops[exit_node] = summed_grads;

  // Wait until we have all exit nodes' backprops collected before processing
  // the while loop.

  *loss = temp_outputs[0];
  *backprop = temp_outputs[1];
  return status;
}

// Op: ReluGrad()
// Summary: Computes rectified linear gradients for a Relu operation.
//
// Description:
Status ReluGrad(AbstractContext* ctx, AbstractTensorHandle* const gradients,
                AbstractTensorHandle* const features,
                AbstractTensorHandle** backprops, const char* name,

    AbstractTensorHandle** backprop, const char* name = nullptr,
    const char* raw_device_name = nullptr);

// Computes rectified linear gradients for a Relu operation.
Status ReluGrad(AbstractContext* ctx, AbstractTensorHandle* const gradients,
                AbstractTensorHandle* const features,
                AbstractTensorHandle** backprops, const char* name = nullptr,

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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...

    # compute with op.
    with backprop.GradientTape() as gt:
      for var_ in vars_:
        gt.watch(var_)
      y = compute_op(**op_kwargs)  # uses op and decomposites by the graph pass.
      grads = gt.gradient(y, vars_)  # uses registered gradient function.

    # compute with composition
    with backprop.GradientTape() as gt:
      for var_ in vars_:
        gt.watch(var_)

  // Build new BackPropFilterOp.
  auto loc = backprop.getLoc();
  auto new_backprop = builder.create<TF::Conv2DBackpropFilterOp>(
      loc, new_result_type, input, new_filter_sizes, backprop.getOutBackprop(),
      strides, backprop.getUseCudnnOnGpu(), backprop.getPadding(),
      backprop.getExplicitPaddings(), backprop.getDataFormat(),
      backprop.getDilations());

  %arg2: tensor<64xf32>
) -> tensor<1x28x28x64xf32> {

  // CHECK: "tf.FusedBatchNormGradV3"
  // CHECK-SAME: (%[[X_TRANSPOSE:[0-9]*]], %[[Y_TRANSPOSE:[0-9]*]],
  // CHECK-SAME: data_format = "NCHW"
  %x_backprop, %scale_backprop, %offset_backprop, %reserve_1, %reserve_2
    = "tf.FusedBatchNormGradV3"(%arg0, %arg1, %arg2, %arg2, %arg2, %arg2)
       {
         data_format = "NHWC",
         epsilon = 1.001 : f32,