"html","_removeOverlay","bind","loadedEvent","overlayAddedEvent","overlayRemovedEvent","Dropdown","toggleSubmenu","next","e","stopPropagation","Toasts","INIT","CREATED","Position","position","fixed","autohide","autoremove","fade","icon","image","imageAlt","imageHeight","title","subtitle","close","body","class","_prepareContainer","initEvent","create","toast","toast_header","toast_image","attr","toast_close","_getContainerId","prepend","createdEvent","removedEvent","container","replace","option"]...

this._config.delay&&500!=this._config.delay&&e.data("delay",this._config.delay);var i=t('<div class="toast-header">');if(null!=this._config.image){var s=t("<img />").addClass("rounded mr-2").attr("src",this._config.image).attr("alt",this._config.imageAlt);null!=this._config.imageHeight&&s.height(this._config.imageHeight).width("auto"),i.append(s)}if(null!=this._config.icon&&i.append(t("<i />").addClass("mr-2").addClass(this._config.icon)),null!=this._config.title&&i.append(t("<strong />").addCla...

        sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
    output = input / (bias + alpha * sqr_sum) ** beta

For details, see [Krizhevsky et al., ImageNet classification with deep
convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).
  }];

  let arguments = (ins
      TFL_FpTensor:$input,
      I32Attr:$radius,

        sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
    output = input / (bias + alpha * sqr_sum) ** beta

For details, see [Krizhevsky et al., ImageNet classification with deep
convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).
  }];

  let arguments = (ins
    Arg<TensorOf<[TF_Bfloat16, TF_Float16, TF_Float32]>, [{4-D.}]>:$input,