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<div class="subTitle"><span class="packageLabelInType">Package</span> <a href="package-summary.html">ai.onnxruntime</a></div>
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<h2 title="Class TensorInfo" class="title">Class TensorInfo</h2>
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<li>java.lang.Object</li>
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<li>ai.onnxruntime.TensorInfo</li>
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<pre>public class <span class="typeNameLabel">TensorInfo</span>
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extends java.lang.Object
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implements <a href="ValueInfo.html" title="interface in ai.onnxruntime">ValueInfo</a></pre>
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<div class="block">Describes an <a href="OnnxTensor.html" title="class in ai.onnxruntime"><code>OnnxTensor</code></a>, including it's size, shape and element type.</div>
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<td class="colFirst"><code>static class </code></td>
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<th class="colSecond" scope="row"><code><span class="memberNameLink"><a href="TensorInfo.OnnxTensorType.html" title="enum in ai.onnxruntime">TensorInfo.OnnxTensorType</a></span></code></th>
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<td class="colLast">
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<div class="block">The native element types supported by the ONNX runtime.</div>
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<th class="colFirst" scope="col">Modifier and Type</th>
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<th class="colSecond" scope="col">Field</th>
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<th class="colLast" scope="col">Description</th>
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<td class="colFirst"><code>static int</code></td>
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<th class="colSecond" scope="row"><code><span class="memberNameLink"><a href="#MAX_DIMENSIONS">MAX_DIMENSIONS</a></span></code></th>
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<td class="colLast">
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<div class="block">Maximum number of dimensions supported by the Java interface methods.</div>
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<td class="colFirst"><code><a href="TensorInfo.OnnxTensorType.html" title="enum in ai.onnxruntime">TensorInfo.OnnxTensorType</a></code></td>
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<th class="colSecond" scope="row"><code><span class="memberNameLink"><a href="#onnxType">onnxType</a></span></code></th>
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<td class="colLast">
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<div class="block">The native type of this tensor.</div>
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<td class="colFirst"><code><a href="OnnxJavaType.html" title="enum in ai.onnxruntime">OnnxJavaType</a></code></td>
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<th class="colSecond" scope="row"><code><span class="memberNameLink"><a href="#type">type</a></span></code></th>
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<td class="colLast">
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<div class="block">The Java type of this tensor.</div>
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</td>
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<caption><span id="t0" class="activeTableTab"><span>All Methods</span><span class="tabEnd"> </span></span><span id="t1" class="tableTab"><span><a href="javascript:show(1);">Static Methods</a></span><span class="tabEnd"> </span></span><span id="t2" class="tableTab"><span><a href="javascript:show(2);">Instance Methods</a></span><span class="tabEnd"> </span></span><span id="t4" class="tableTab"><span><a href="javascript:show(8);">Concrete Methods</a></span><span class="tabEnd"> </span></span></caption>
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<th class="colFirst" scope="col">Modifier and Type</th>
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<th class="colSecond" scope="col">Method</th>
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<th class="colLast" scope="col">Description</th>
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<td class="colFirst"><code>static <a href="TensorInfo.html" title="class in ai.onnxruntime">TensorInfo</a></code></td>
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<th class="colSecond" scope="row"><code><span class="memberNameLink"><a href="#constructFromBuffer(java.nio.Buffer,long%5B%5D,ai.onnxruntime.OnnxJavaType)">constructFromBuffer</a></span>​(java.nio.Buffer buffer,
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long[] shape,
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<a href="OnnxJavaType.html" title="enum in ai.onnxruntime">OnnxJavaType</a> type)</code></th>
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<td class="colLast">
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<div class="block">Constructs a TensorInfo from the supplied byte buffer.</div>
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<td class="colFirst"><code>static <a href="TensorInfo.html" title="class in ai.onnxruntime">TensorInfo</a></code></td>
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<th class="colSecond" scope="row"><code><span class="memberNameLink"><a href="#constructFromJavaArray(java.lang.Object)">constructFromJavaArray</a></span>​(java.lang.Object obj)</code></th>
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<td class="colLast">
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<div class="block">Constructs a TensorInfo from the supplied multidimensional Java array, used to allocate the
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appropriate amount of native memory.</div>
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</td>
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<tr id="i2" class="altColor">
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<td class="colFirst"><code>static <T extends java.nio.Buffer><br><a href="TensorInfo.html" title="class in ai.onnxruntime">TensorInfo</a></code></td>
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<th class="colSecond" scope="row"><code><span class="memberNameLink"><a href="#constructFromSparseTensor(ai.onnxruntime.OnnxSparseTensor.SparseTensor)">constructFromSparseTensor</a></span>​(<a href="OnnxSparseTensor.SparseTensor.html" title="class in ai.onnxruntime">OnnxSparseTensor.SparseTensor</a><T> tensor)</code></th>
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<td class="colLast">
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<div class="block">Constructs a TensorInfo from the supplied <a href="OnnxSparseTensor.SparseTensor.html" title="class in ai.onnxruntime"><code>OnnxSparseTensor.SparseTensor</code></a>.</div>
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</td>
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<td class="colFirst"><code>long[]</code></td>
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<th class="colSecond" scope="row"><code><span class="memberNameLink"><a href="#getShape()">getShape</a></span>()</code></th>
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<td class="colLast">
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<div class="block">Get a copy of the tensor's shape.</div>
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<tr id="i4" class="altColor">
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<td class="colFirst"><code>boolean</code></td>
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<th class="colSecond" scope="row"><code><span class="memberNameLink"><a href="#isScalar()">isScalar</a></span>()</code></th>
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<td class="colLast">
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<div class="block">Returns true if the shape represents a scalar value (i.e.</div>
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</td>
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<tr id="i5" class="rowColor">
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<td class="colFirst"><code>java.lang.Object</code></td>
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<th class="colSecond" scope="row"><code><span class="memberNameLink"><a href="#makeCarrier()">makeCarrier</a></span>()</code></th>
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<td class="colLast">
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<div class="block">Constructs an array the right shape and type to hold this tensor.</div>
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</td>
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<tr id="i6" class="altColor">
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<td class="colFirst"><code>java.lang.String</code></td>
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<th class="colSecond" scope="row"><code><span class="memberNameLink"><a href="#toString()">toString</a></span>()</code></th>
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<h3>Methods inherited from class java.lang.Object</h3>
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<code>clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait</code></li>
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<pre>public static final int MAX_DIMENSIONS</pre>
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<div class="block">Maximum number of dimensions supported by the Java interface methods.</div>
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<dl>
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<dt><span class="seeLabel">See Also:</span></dt>
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<dd><a href="../../constant-values.html#ai.onnxruntime.TensorInfo.MAX_DIMENSIONS">Constant Field Values</a></dd>
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</dl>
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<pre>public final <a href="OnnxJavaType.html" title="enum in ai.onnxruntime">OnnxJavaType</a> type</pre>
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<div class="block">The Java type of this tensor.</div>
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<h4>onnxType</h4>
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<pre>public final <a href="TensorInfo.OnnxTensorType.html" title="enum in ai.onnxruntime">TensorInfo.OnnxTensorType</a> onnxType</pre>
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<div class="block">The native type of this tensor.</div>
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<h4>getShape</h4>
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<pre class="methodSignature">public long[] getShape()</pre>
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<div class="block">Get a copy of the tensor's shape.</div>
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<dl>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>A copy of the tensor's shape.</dd>
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<pre class="methodSignature">public java.lang.String toString()</pre>
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<dd><code>toString</code> in class <code>java.lang.Object</code></dd>
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<pre class="methodSignature">public boolean isScalar()</pre>
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<div class="block">Returns true if the shape represents a scalar value (i.e. it has zero dimensions).</div>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>True if the shape is a scalar.</dd>
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<pre class="methodSignature">public java.lang.Object makeCarrier()
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throws <a href="OrtException.html" title="class in ai.onnxruntime">OrtException</a></pre>
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<div class="block">Constructs an array the right shape and type to hold this tensor.
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<p>Note for String tensors, this carrier is a single dimensional array with enough space for
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all elements as that's the expected format of the native code. It can be reshaped to the
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correct shape using <a href="OrtUtil.html#reshape(java.lang.String%5B%5D,long%5B%5D)"><code>OrtUtil.reshape(String[],long[])</code></a>.</div>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>A multidimensional array of the appropriate primitive type (or String).</dd>
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<dt><span class="throwsLabel">Throws:</span></dt>
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<dd><code><a href="OrtException.html" title="class in ai.onnxruntime">OrtException</a></code> - If the shape isn't representable in Java (i.e. if one of it's indices is
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greater than an int).</dd>
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<pre class="methodSignature">public static <a href="TensorInfo.html" title="class in ai.onnxruntime">TensorInfo</a> constructFromJavaArray​(java.lang.Object obj)
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throws <a href="OrtException.html" title="class in ai.onnxruntime">OrtException</a></pre>
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<div class="block">Constructs a TensorInfo from the supplied multidimensional Java array, used to allocate the
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appropriate amount of native memory.</div>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>obj</code> - The object to inspect.</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>A TensorInfo which can be used to make the right size Tensor.</dd>
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<dt><span class="throwsLabel">Throws:</span></dt>
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<dd><code><a href="OrtException.html" title="class in ai.onnxruntime">OrtException</a></code> - If the supplied Object isn't an array, or is an invalid type.</dd>
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</dl>
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</li>
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</ul>
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<a id="constructFromBuffer(java.nio.Buffer,long[],ai.onnxruntime.OnnxJavaType)">
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<!-- -->
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</a>
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<ul class="blockList">
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<h4>constructFromBuffer</h4>
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<pre class="methodSignature">public static <a href="TensorInfo.html" title="class in ai.onnxruntime">TensorInfo</a> constructFromBuffer​(java.nio.Buffer buffer,
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long[] shape,
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<a href="OnnxJavaType.html" title="enum in ai.onnxruntime">OnnxJavaType</a> type)
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throws <a href="OrtException.html" title="class in ai.onnxruntime">OrtException</a></pre>
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<div class="block">Constructs a TensorInfo from the supplied byte buffer.</div>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>buffer</code> - The buffer to inspect.</dd>
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<dd><code>shape</code> - The shape of the tensor.</dd>
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<dd><code>type</code> - The Java type.</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>A TensorInfo for a tensor.</dd>
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<dt><span class="throwsLabel">Throws:</span></dt>
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<dd><code><a href="OrtException.html" title="class in ai.onnxruntime">OrtException</a></code> - If the supplied buffer doesn't match the shape.</dd>
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</dl>
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</li>
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</ul>
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<a id="constructFromSparseTensor(ai.onnxruntime.OnnxSparseTensor.SparseTensor)">
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<!-- -->
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</a>
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<ul class="blockListLast">
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<li class="blockList">
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<h4>constructFromSparseTensor</h4>
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<pre class="methodSignature">public static <T extends java.nio.Buffer> <a href="TensorInfo.html" title="class in ai.onnxruntime">TensorInfo</a> constructFromSparseTensor​(<a href="OnnxSparseTensor.SparseTensor.html" title="class in ai.onnxruntime">OnnxSparseTensor.SparseTensor</a><T> tensor)
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throws <a href="OrtException.html" title="class in ai.onnxruntime">OrtException</a></pre>
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<div class="block">Constructs a TensorInfo from the supplied <a href="OnnxSparseTensor.SparseTensor.html" title="class in ai.onnxruntime"><code>OnnxSparseTensor.SparseTensor</code></a>.</div>
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<dl>
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<dt><span class="paramLabel">Type Parameters:</span></dt>
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<dd><code>T</code> - The buffer type.</dd>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>tensor</code> - The sparse tensor.</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>A TensorInfo for a sparse tensor.</dd>
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<dt><span class="throwsLabel">Throws:</span></dt>
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<dd><code><a href="OrtException.html" title="class in ai.onnxruntime">OrtException</a></code> - If the supplied tensor has too many elements for it's shape.</dd>
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</dl>
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