import{s as xn,f as jn,o as In,n as ze}from"../chunks/scheduler.9bc65507.js";import{S as Jn,i as kn,g as o,s as i,u as b,A as Fn,h as r,f as t,c,j as g,v as w,y as f,k as h,o as d,a as s,w as y,d as $,t as M,x as C}from"../chunks/index.78c82d43.js";import{T as Bt}from"../chunks/Tip.87d55b76.js";import{D as Ye}from"../chunks/Docstring.4e7352e2.js";import{C as St}from"../chunks/CodeBlock.73e038be.js";import{I as Ae}from"../chunks/IconCopyLink.bedaa44d.js";import{E as Rt}from"../chunks/ExampleCodeBlock.872b014d.js";import{P as Zn}from"../chunks/PipelineTag.75b0f707.js";function Nn(T){let l,v="This model is in maintenance mode only, so we won’t accept any new PRs changing its code.",m,p,u=`If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: <code>pip install -U transformers==4.30.0</code>.`;return{c(){l=o("p"),l.textContent=v,m=i(),p=o("p"),p.innerHTML=u},l(a){l=r(a,"P",{"data-svelte-h":!0}),f(l)!=="svelte-lwu440"&&(l.textContent=v),m=c(a),p=r(a,"P",{"data-svelte-h":!0}),f(p)!=="svelte-4042uy"&&(p.innerHTML=u)},m(a,_){s(a,l,_),s(a,m,_),s(a,p,_)},p:ze,d(a){a&&(t(l),t(m),t(p))}}}function Wn(T){let l,v="Example:",m,p,u;return p=new St({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFZhbk1vZGVsJTJDJTIwVmFuQ29uZmlnJTBBJTBBJTIzJTIwSW5pdGlhbGl6aW5nJTIwYSUyMFZBTiUyMHZhbi1iYXNlJTIwc3R5bGUlMjBjb25maWd1cmF0aW9uJTBBY29uZmlndXJhdGlvbiUyMCUzRCUyMFZhbkNvbmZpZygpJTBBJTIzJTIwSW5pdGlhbGl6aW5nJTIwYSUyMG1vZGVsJTIwZnJvbSUyMHRoZSUyMHZhbi1iYXNlJTIwc3R5bGUlMjBjb25maWd1cmF0aW9uJTBBbW9kZWwlMjAlM0QlMjBWYW5Nb2RlbChjb25maWd1cmF0aW9uKSUwQSUyMyUyMEFjY2Vzc2luZyUyMHRoZSUyMG1vZGVsJTIwY29uZmlndXJhdGlvbiUwQWNvbmZpZ3VyYXRpb24lMjAlM0QlMjBtb2RlbC5jb25maWc=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> VanModel, VanConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a VAN van-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = VanConfig()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the van-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = VanModel(configuration)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`,wrap:!1}}),{c(){l=o("p"),l.textContent=v,m=i(),b(p.$$.fragment)},l(a){l=r(a,"P",{"data-svelte-h":!0}),f(l)!=="svelte-11lpom8"&&(l.textContent=v),m=c(a),w(p.$$.fragment,a)},m(a,_){s(a,l,_),s(a,m,_),y(p,a,_),u=!0},p:ze,i(a){u||($(p.$$.fragment,a),u=!0)},o(a){M(p.$$.fragment,a),u=!1},d(a){a&&(t(l),t(m)),C(p,a)}}}function Un(T){let l,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){l=o("p"),l.innerHTML=v},l(m){l=r(m,"P",{"data-svelte-h":!0}),f(l)!=="svelte-fincs2"&&(l.innerHTML=v)},m(m,p){s(m,l,p)},p:ze,d(m){m&&t(l)}}}function An(T){let l,v="Example:",m,p,u;return p=new St({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, VanModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;Visual-Attention-Network/van-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = VanModel.from_pretrained(<span class="hljs-string">&quot;Visual-Attention-Network/van-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">512</span>, <span class="hljs-number">7</span>, <span class="hljs-number">7</span>]`,wrap:!1}}),{c(){l=o("p"),l.textContent=v,m=i(),b(p.$$.fragment)},l(a){l=r(a,"P",{"data-svelte-h":!0}),f(l)!=="svelte-11lpom8"&&(l.textContent=v),m=c(a),w(p.$$.fragment,a)},m(a,_){s(a,l,_),s(a,m,_),y(p,a,_),u=!0},p:ze,i(a){u||($(p.$$.fragment,a),u=!0)},o(a){M(p.$$.fragment,a),u=!1},d(a){a&&(t(l),t(m)),C(p,a)}}}function zn(T){let l,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){l=o("p"),l.innerHTML=v},l(m){l=r(m,"P",{"data-svelte-h":!0}),f(l)!=="svelte-fincs2"&&(l.innerHTML=v)},m(m,p){s(m,l,p)},p:ze,d(m){m&&t(l)}}}function Pn(T){let l,v="Example:",m,p,u;return p=new St({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9JbWFnZVByb2Nlc3NvciUyQyUyMFZhbkZvckltYWdlQ2xhc3NpZmljYXRpb24lMEFpbXBvcnQlMjB0b3JjaCUwQWZyb20lMjBkYXRhc2V0cyUyMGltcG9ydCUyMGxvYWRfZGF0YXNldCUwQSUwQWRhdGFzZXQlMjAlM0QlMjBsb2FkX2RhdGFzZXQoJTIyaHVnZ2luZ2ZhY2UlMkZjYXRzLWltYWdlJTIyKSUwQWltYWdlJTIwJTNEJTIwZGF0YXNldCU1QiUyMnRlc3QlMjIlNUQlNUIlMjJpbWFnZSUyMiU1RCU1QjAlNUQlMEElMEFpbWFnZV9wcm9jZXNzb3IlMjAlM0QlMjBBdXRvSW1hZ2VQcm9jZXNzb3IuZnJvbV9wcmV0cmFpbmVkKCUyMlZpc3VhbC1BdHRlbnRpb24tTmV0d29yayUyRnZhbi1iYXNlJTIyKSUwQW1vZGVsJTIwJTNEJTIwVmFuRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyVmlzdWFsLUF0dGVudGlvbi1OZXR3b3JrJTJGdmFuLWJhc2UlMjIpJTBBJTBBaW5wdXRzJTIwJTNEJTIwaW1hZ2VfcHJvY2Vzc29yKGltYWdlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiklMEElMEF3aXRoJTIwdG9yY2gubm9fZ3JhZCgpJTNBJTBBJTIwJTIwJTIwJTIwbG9naXRzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMpLmxvZ2l0cyUwQSUwQSUyMyUyMG1vZGVsJTIwcHJlZGljdHMlMjBvbmUlMjBvZiUyMHRoZSUyMDEwMDAlMjBJbWFnZU5ldCUyMGNsYXNzZXMlMEFwcmVkaWN0ZWRfbGFiZWwlMjAlM0QlMjBsb2dpdHMuYXJnbWF4KC0xKS5pdGVtKCklMEFwcmludChtb2RlbC5jb25maWcuaWQybGFiZWwlNUJwcmVkaWN0ZWRfbGFiZWwlNUQp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, VanForImageClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;Visual-Attention-Network/van-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = VanForImageClassification.from_pretrained(<span class="hljs-string">&quot;Visual-Attention-Network/van-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># model predicts one of the 1000 ImageNet classes</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(model.config.id2label[predicted_label])
tabby, tabby cat`,wrap:!1}}),{c(){l=o("p"),l.textContent=v,m=i(),b(p.$$.fragment)},l(a){l=r(a,"P",{"data-svelte-h":!0}),f(l)!=="svelte-11lpom8"&&(l.textContent=v),m=c(a),w(p.$$.fragment,a)},m(a,_){s(a,l,_),s(a,m,_),y(p,a,_),u=!0},p:ze,i(a){u||($(p.$$.fragment,a),u=!0)},o(a){M(p.$$.fragment,a),u=!1},d(a){a&&(t(l),t(m)),C(p,a)}}}function En(T){let l,v,m,p,u,a,_,D,Ct,Me,Xt="VAN",Qe,U,De,k,A,Pe,O,Tt,Ce,qt="Overview",Oe,K,Yt='The VAN model was proposed in <a href="https://arxiv.org/abs/2202.09741" rel="nofollow">Visual Attention Network</a> by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.',Ke,ee,Qt="This paper introduces a new attention layer based on convolution operations able to capture both local and distant relationships. This is done by combining normal and large kernel convolution layers. The latter uses a dilated convolution to capture distant correlations.",et,te,Dt="The abstract from the paper is the following:",tt,ne,Ot='<em>While originally designed for natural language processing tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple, VAN outperforms the state-of-the-art vision transformers and convolutional neural networks with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc. Code is available at <a href="https://github.com/Visual-Attention-Network/VAN-Classification" rel="nofollow">this https URL</a>.</em>',nt,ae,Kt="Tips:",at,se,en="<li>VAN does not have an embedding layer, thus the <code>hidden_states</code> will have a length equal to the number of stages.</li>",st,oe,tn='The figure below illustrates the architecture of a Visual Aattention Layer. Taken from the <a href="https://arxiv.org/abs/2202.09741" rel="nofollow">original paper</a>.',ot,re,nn,rt,le,an='This model was contributed by <a href="https://huggingface.co/Francesco" rel="nofollow">Francesco</a>. The original code can be found <a href="https://github.com/Visual-Attention-Network/VAN-Classification" rel="nofollow">here</a>.',lt,F,z,Ee,ie,Vt,Te,sn="Resources",it,ce,on="A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with VAN.",ct,de,dt,pe,rn='<li><a href="/docs/transformers/v4.34.0/en/model_doc/van#transformers.VanForImageClassification">VanForImageClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb" rel="nofollow">notebook</a>.</li> <li>See also: <a href="../tasks/image_classification">Image classification task guide</a></li>',pt,me,ln="If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.",mt,Z,P,He,he,xt,Ve,cn="VanConfig",ht,V,fe,jt,xe,dn=`This is the configuration class to store the configuration of a <a href="/docs/transformers/v4.34.0/en/model_doc/van#transformers.VanModel">VanModel</a>. It is used to instantiate a VAN model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the VAN
<a href="https://huggingface.co/Visual-Attention-Network/van-base" rel="nofollow">Visual-Attention-Network/van-base</a> architecture.`,It,je,pn=`Configuration objects inherit from <a href="/docs/transformers/v4.34.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/v4.34.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Jt,E,ft,N,H,Ge,ue,kt,Ie,mn="VanModel",ut,J,ge,Ft,Je,hn=`The bare VAN model outputting raw features without any specific head on top. Note, VAN does not have an embedding layer.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,Zt,j,ve,Nt,ke,fn='The <a href="/docs/transformers/v4.34.0/en/model_doc/van#transformers.VanModel">VanModel</a> forward method, overrides the <code>__call__</code> special method.',Wt,G,Ut,L,gt,W,B,Le,_e,At,Fe,un="VanForImageClassification",vt,x,be,zt,Ze,gn=`VAN Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.`,Pt,Ne,vn=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,Et,I,we,Ht,We,_n='The <a href="/docs/transformers/v4.34.0/en/model_doc/van#transformers.VanForImageClassification">VanForImageClassification</a> forward method, overrides the <code>__call__</code> special method.',Gt,R,Lt,S,_t,Be,bt;return D=new Ae({}),U=new Bt({props:{warning:!0,$$slots:{default:[Nn]},$$scope:{ctx:T}}}),O=new Ae({}),ie=new Ae({}),de=new Zn({props:{pipeline:"image-classification"}}),he=new Ae({}),fe=new Ye({props:{name:"class transformers.VanConfig",anchor:"transformers.VanConfig",parameters:[{name:"image_size",val:" = 224"},{name:"num_channels",val:" = 3"},{name:"patch_sizes",val:" = [7, 3, 3, 3]"},{name:"strides",val:" = [4, 2, 2, 2]"},{name:"hidden_sizes",val:" = [64, 128, 320, 512]"},{name:"depths",val:" = [3, 3, 12, 3]"},{name:"mlp_ratios",val:" = [8, 8, 4, 4]"},{name:"hidden_act",val:" = 'gelu'"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-06"},{name:"layer_scale_init_value",val:" = 0.01"},{name:"drop_path_rate",val:" = 0.0"},{name:"dropout_rate",val:" = 0.0"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.VanConfig.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 224) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.VanConfig.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The number of input channels.`,name:"num_channels"},{anchor:"transformers.VanConfig.patch_sizes",description:`<strong>patch_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[7, 3, 3, 3]</code>) &#x2014;
Patch size to use in each stage&#x2019;s embedding layer.`,name:"patch_sizes"},{anchor:"transformers.VanConfig.strides",description:`<strong>strides</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[4, 2, 2, 2]</code>) &#x2014;
Stride size to use in each stage&#x2019;s embedding layer to downsample the input.`,name:"strides"},{anchor:"transformers.VanConfig.hidden_sizes",description:`<strong>hidden_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[64, 128, 320, 512]</code>) &#x2014;
Dimensionality (hidden size) at each stage.`,name:"hidden_sizes"},{anchor:"transformers.VanConfig.depths",description:`<strong>depths</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[3, 3, 12, 3]</code>) &#x2014;
Depth (number of layers) for each stage.`,name:"depths"},{anchor:"transformers.VanConfig.mlp_ratios",description:`<strong>mlp_ratios</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[8, 8, 4, 4]</code>) &#x2014;
The expansion ratio for mlp layer at each stage.`,name:"mlp_ratios"},{anchor:"transformers.VanConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in each layer. If string, <code>&quot;gelu&quot;</code>, <code>&quot;relu&quot;</code>,
<code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.VanConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.VanConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.VanConfig.layer_scale_init_value",description:`<strong>layer_scale_init_value</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-2) &#x2014;
The initial value for layer scaling.`,name:"layer_scale_init_value"},{anchor:"transformers.VanConfig.drop_path_rate",description:`<strong>drop_path_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for stochastic depth.`,name:"drop_path_rate"},{anchor:"transformers.VanConfig.dropout_rate",description:`<strong>dropout_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for dropout.`,name:"dropout_rate"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/deprecated/van/configuration_van.py#L30"}}),E=new Rt({props:{anchor:"transformers.VanConfig.example",$$slots:{default:[Wn]},$$scope:{ctx:T}}}),ue=new Ae({}),ge=new Ye({props:{name:"class transformers.VanModel",anchor:"transformers.VanModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.VanModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.34.0/en/model_doc/van#transformers.VanConfig">VanConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.34.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
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<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) — Last layer hidden-state after a pooling operation on the spatial dimensions.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, num_channels, height, width)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
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<li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Classification (or regression if config.num_labels==1) scores (before SoftMax).</li>
<li><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape <code>(batch_size, num_channels, height, width)</code>. Hidden-states (also
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`,returnType:`
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