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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
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<title>Tensor (pytorch_android 1.9.0 API)</title>
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var methods = {"i0":9,"i1":9,"i2":9,"i3":9,"i4":9,"i5":6,"i6":9,"i7":9,"i8":9,"i9":9,"i10":9,"i11":9,"i12":9,"i13":9,"i14":9,"i15":9,"i16":9,"i17":9,"i18":9,"i19":9,"i20":9,"i21":9,"i22":9,"i23":9,"i24":9,"i25":9,"i26":9,"i27":9,"i28":9,"i29":9,"i30":10,"i31":10,"i32":10,"i33":10,"i34":10,"i35":10,"i36":10,"i37":10,"i38":9,"i39":10};
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<ul class="subNavList">
<li>Summary: </li>
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<div class="subTitle">org.pytorch</div>
<h2 title="Class Tensor" class="title">Class Tensor</h2>
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<ul class="inheritance">
<li>java.lang.Object</li>
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<li>org.pytorch.Tensor</li>
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<pre>public abstract class <span class="typeNameLabel">Tensor</span>
extends java.lang.Object</pre>
<div class="block">Representation of a Tensor. Behavior is similar to PyTorch's tensor objects.
<p>Most tensors will be constructed as <code>Tensor.fromBlob(data, shape)</code>, where <code>data</code>
can be an array or a direct <code>Buffer</code> (of the proper subclass). Helper methods are provided
to allocate buffers properly.
<p>To access Tensor data, see <a href="../../org/pytorch/Tensor.html#dtype--"><code>dtype()</code></a>, <a href="../../org/pytorch/Tensor.html#shape--"><code>shape()</code></a>, and various <code>getDataAs*</code>
methods.
<p>When constructing <code>Tensor</code> objects with <code>data</code> as an array, it is not specified
whether this data is is copied or retained as a reference so it is recommended not to modify it
after constructing. <code>data</code> passed as a <code>Buffer</code> is not copied, so it can be modified
between <a href="../../org/pytorch/Module.html" title="class in org.pytorch"><code>Module</code></a> calls to avoid reallocation. Data retrieved from <code>Tensor</code> objects
may be copied or may be a reference to the <code>Tensor</code>'s internal data buffer. <code>shape</code>
is always copied.</div>
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<h3>Method Summary</h3>
<table class="memberSummary" border="0" cellpadding="3" cellspacing="0" summary="Method Summary table, listing methods, and an explanation">
<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="t3" class="tableTab"><span><a href="javascript:show(4);">Abstract 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>
<tr>
<th class="colFirst" scope="col">Modifier and Type</th>
<th class="colLast" scope="col">Method and Description</th>
</tr>
<tr id="i0" class="altColor">
<td class="colFirst"><code>static java.nio.ByteBuffer</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#allocateByteBuffer-int-">allocateByteBuffer</a></span>(int numElements)</code>
<div class="block">Allocates a new direct <code>ByteBuffer</code> with native byte order with specified
capacity that can be used in <a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.ByteBuffer-long:A-"><code>fromBlob(ByteBuffer, long[])</code></a>, <a href="../../org/pytorch/Tensor.html#fromBlobUnsigned-java.nio.ByteBuffer-long:A-"><code>fromBlobUnsigned(ByteBuffer, long[])</code></a>.</div>
</td>
</tr>
<tr id="i1" class="rowColor">
<td class="colFirst"><code>static java.nio.DoubleBuffer</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#allocateDoubleBuffer-int-">allocateDoubleBuffer</a></span>(int numElements)</code>
<div class="block">Allocates a new direct <code>DoubleBuffer</code> with native byte order with specified
capacity that can be used in <a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.DoubleBuffer-long:A-"><code>fromBlob(DoubleBuffer, long[])</code></a>.</div>
</td>
</tr>
<tr id="i2" class="altColor">
<td class="colFirst"><code>static java.nio.FloatBuffer</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#allocateFloatBuffer-int-">allocateFloatBuffer</a></span>(int numElements)</code>
<div class="block">Allocates a new direct <code>FloatBuffer</code> with native byte order with specified
capacity that can be used in <a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.FloatBuffer-long:A-"><code>fromBlob(FloatBuffer, long[])</code></a>.</div>
</td>
</tr>
<tr id="i3" class="rowColor">
<td class="colFirst"><code>static java.nio.IntBuffer</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#allocateIntBuffer-int-">allocateIntBuffer</a></span>(int numElements)</code>
<div class="block">Allocates a new direct <code>IntBuffer</code> with native byte order with specified
capacity that can be used in <a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.IntBuffer-long:A-"><code>fromBlob(IntBuffer, long[])</code></a>.</div>
</td>
</tr>
<tr id="i4" class="altColor">
<td class="colFirst"><code>static java.nio.LongBuffer</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#allocateLongBuffer-int-">allocateLongBuffer</a></span>(int numElements)</code>
<div class="block">Allocates a new direct <code>LongBuffer</code> with native byte order with specified
capacity that can be used in <a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.LongBuffer-long:A-"><code>fromBlob(LongBuffer, long[])</code></a>.</div>
</td>
</tr>
<tr id="i5" class="rowColor">
<td class="colFirst"><code>abstract <a href="../../org/pytorch/DType.html" title="enum in org.pytorch">DType</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#dtype--">dtype</a></span>()</code> </td>
</tr>
<tr id="i6" class="altColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-byte:A-long:A-">fromBlob</a></span>(byte[] data,
long[] shape)</code> </td>
</tr>
<tr id="i7" class="rowColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-byte:A-long:A-org.pytorch.MemoryFormat-">fromBlob</a></span>(byte[] data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</code>
<div class="block">Creates a new Tensor instance with dtype torch.int8 with specified shape and data as array of
bytes.</div>
</td>
</tr>
<tr id="i8" class="altColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.ByteBuffer-long:A-">fromBlob</a></span>(java.nio.ByteBuffer data,
long[] shape)</code> </td>
</tr>
<tr id="i9" class="rowColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.ByteBuffer-long:A-org.pytorch.MemoryFormat-">fromBlob</a></span>(java.nio.ByteBuffer data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</code>
<div class="block">Creates a new Tensor instance with dtype torch.int8 with specified shape and data.</div>
</td>
</tr>
<tr id="i10" class="altColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-double:A-long:A-">fromBlob</a></span>(double[] data,
long[] shape)</code> </td>
</tr>
<tr id="i11" class="rowColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-double:A-long:A-org.pytorch.MemoryFormat-">fromBlob</a></span>(double[] data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</code>
<div class="block">Creates a new Tensor instance with dtype torch.float64 with specified shape and data as array
of doubles.</div>
</td>
</tr>
<tr id="i12" class="altColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.DoubleBuffer-long:A-">fromBlob</a></span>(java.nio.DoubleBuffer data,
long[] shape)</code> </td>
</tr>
<tr id="i13" class="rowColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.DoubleBuffer-long:A-org.pytorch.MemoryFormat-">fromBlob</a></span>(java.nio.DoubleBuffer data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</code>
<div class="block">Creates a new Tensor instance with dtype torch.float64 with specified shape and data.</div>
</td>
</tr>
<tr id="i14" class="altColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-float:A-long:A-">fromBlob</a></span>(float[] data,
long[] shape)</code> </td>
</tr>
<tr id="i15" class="rowColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-float:A-long:A-org.pytorch.MemoryFormat-">fromBlob</a></span>(float[] data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</code>
<div class="block">Creates a new Tensor instance with dtype torch.float32 with specified shape and data as array
of floats.</div>
</td>
</tr>
<tr id="i16" class="altColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.FloatBuffer-long:A-">fromBlob</a></span>(java.nio.FloatBuffer data,
long[] shape)</code> </td>
</tr>
<tr id="i17" class="rowColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.FloatBuffer-long:A-org.pytorch.MemoryFormat-">fromBlob</a></span>(java.nio.FloatBuffer data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</code>
<div class="block">Creates a new Tensor instance with dtype torch.float32 with specified shape and data.</div>
</td>
</tr>
<tr id="i18" class="altColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-int:A-long:A-">fromBlob</a></span>(int[] data,
long[] shape)</code> </td>
</tr>
<tr id="i19" class="rowColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-int:A-long:A-org.pytorch.MemoryFormat-">fromBlob</a></span>(int[] data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</code>
<div class="block">Creates a new Tensor instance with dtype torch.int32 with specified shape and data as array of
ints.</div>
</td>
</tr>
<tr id="i20" class="altColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.IntBuffer-long:A-">fromBlob</a></span>(java.nio.IntBuffer data,
long[] shape)</code> </td>
</tr>
<tr id="i21" class="rowColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.IntBuffer-long:A-org.pytorch.MemoryFormat-">fromBlob</a></span>(java.nio.IntBuffer data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</code>
<div class="block">Creates a new Tensor instance with dtype torch.int32 with specified shape and data.</div>
</td>
</tr>
<tr id="i22" class="altColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-long:A-long:A-">fromBlob</a></span>(long[] data,
long[] shape)</code> </td>
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<tr id="i23" class="rowColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-long:A-long:A-org.pytorch.MemoryFormat-">fromBlob</a></span>(long[] data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</code>
<div class="block">Creates a new Tensor instance with dtype torch.int64 with specified shape and data as array of
longs.</div>
</td>
</tr>
<tr id="i24" class="altColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.LongBuffer-long:A-">fromBlob</a></span>(java.nio.LongBuffer data,
long[] shape)</code> </td>
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<tr id="i25" class="rowColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.LongBuffer-long:A-org.pytorch.MemoryFormat-">fromBlob</a></span>(java.nio.LongBuffer data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</code>
<div class="block">Creates a new Tensor instance with dtype torch.int64 with specified shape and data.</div>
</td>
</tr>
<tr id="i26" class="altColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlobUnsigned-byte:A-long:A-">fromBlobUnsigned</a></span>(byte[] data,
long[] shape)</code> </td>
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<tr id="i27" class="rowColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlobUnsigned-byte:A-long:A-org.pytorch.MemoryFormat-">fromBlobUnsigned</a></span>(byte[] data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</code>
<div class="block">Creates a new Tensor instance with dtype torch.uint8 with specified shape and data as array of
bytes.</div>
</td>
</tr>
<tr id="i28" class="altColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlobUnsigned-java.nio.ByteBuffer-long:A-">fromBlobUnsigned</a></span>(java.nio.ByteBuffer data,
long[] shape)</code> </td>
</tr>
<tr id="i29" class="rowColor">
<td class="colFirst"><code>static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#fromBlobUnsigned-java.nio.ByteBuffer-long:A-org.pytorch.MemoryFormat-">fromBlobUnsigned</a></span>(java.nio.ByteBuffer data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</code>
<div class="block">Creates a new Tensor instance with dtype torch.uint8 with specified shape and data.</div>
</td>
</tr>
<tr id="i30" class="altColor">
<td class="colFirst"><code>byte[]</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#getDataAsByteArray--">getDataAsByteArray</a></span>()</code> </td>
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<tr id="i31" class="rowColor">
<td class="colFirst"><code>double[]</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#getDataAsDoubleArray--">getDataAsDoubleArray</a></span>()</code> </td>
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<tr id="i32" class="altColor">
<td class="colFirst"><code>float[]</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#getDataAsFloatArray--">getDataAsFloatArray</a></span>()</code> </td>
</tr>
<tr id="i33" class="rowColor">
<td class="colFirst"><code>int[]</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#getDataAsIntArray--">getDataAsIntArray</a></span>()</code> </td>
</tr>
<tr id="i34" class="altColor">
<td class="colFirst"><code>long[]</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#getDataAsLongArray--">getDataAsLongArray</a></span>()</code> </td>
</tr>
<tr id="i35" class="rowColor">
<td class="colFirst"><code>byte[]</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#getDataAsUnsignedByteArray--">getDataAsUnsignedByteArray</a></span>()</code> </td>
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<tr id="i36" class="altColor">
<td class="colFirst"><code><a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#memoryFormat--">memoryFormat</a></span>()</code>
<div class="block">Returns the memory format of this tensor.</div>
</td>
</tr>
<tr id="i37" class="rowColor">
<td class="colFirst"><code>long</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#numel--">numel</a></span>()</code>
<div class="block">Returns the number of elements in this tensor.</div>
</td>
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<tr id="i38" class="altColor">
<td class="colFirst"><code>static long</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#numel-long:A-">numel</a></span>(long[] shape)</code>
<div class="block">Calculates the number of elements in a tensor with the specified shape.</div>
</td>
</tr>
<tr id="i39" class="rowColor">
<td class="colFirst"><code>long[]</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../org/pytorch/Tensor.html#shape--">shape</a></span>()</code>
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<pre>public static java.nio.IntBuffer allocateIntBuffer(int numElements)</pre>
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capacity that can be used in <a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.IntBuffer-long:A-"><code>fromBlob(IntBuffer, long[])</code></a>.</div>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<pre>public static java.nio.FloatBuffer allocateFloatBuffer(int numElements)</pre>
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capacity that can be used in <a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.FloatBuffer-long:A-"><code>fromBlob(FloatBuffer, long[])</code></a>.</div>
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<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>numElements</code> - capacity (number of elements) of result buffer.</dd>
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<pre>public static java.nio.LongBuffer allocateLongBuffer(int numElements)</pre>
<div class="block">Allocates a new direct <code>LongBuffer</code> with native byte order with specified
capacity that can be used in <a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.LongBuffer-long:A-"><code>fromBlob(LongBuffer, long[])</code></a>.</div>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<pre>public static java.nio.DoubleBuffer allocateDoubleBuffer(int numElements)</pre>
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capacity that can be used in <a href="../../org/pytorch/Tensor.html#fromBlob-java.nio.DoubleBuffer-long:A-"><code>fromBlob(DoubleBuffer, long[])</code></a>.</div>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlobUnsigned(byte[] data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</pre>
<div class="block">Creates a new Tensor instance with dtype torch.uint8 with specified shape and data as array of
bytes.</div>
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<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>data</code> - Tensor elements</dd>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlobUnsigned(byte[] data,
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(byte[] data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</pre>
<div class="block">Creates a new Tensor instance with dtype torch.int8 with specified shape and data as array of
bytes.</div>
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<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>data</code> - Tensor elements</dd>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(byte[] data,
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(int[] data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</pre>
<div class="block">Creates a new Tensor instance with dtype torch.int32 with specified shape and data as array of
ints.</div>
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<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>data</code> - Tensor elements</dd>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(int[] data,
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(float[] data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</pre>
<div class="block">Creates a new Tensor instance with dtype torch.float32 with specified shape and data as array
of floats.</div>
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<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>data</code> - Tensor elements</dd>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(float[] data,
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(long[] data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</pre>
<div class="block">Creates a new Tensor instance with dtype torch.int64 with specified shape and data as array of
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<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>data</code> - Tensor elements</dd>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(long[] data,
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(double[] data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</pre>
<div class="block">Creates a new Tensor instance with dtype torch.float64 with specified shape and data as array
of doubles.</div>
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<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>shape</code> - Tensor shape</dd>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(double[] data,
long[] shape)</pre>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlobUnsigned(java.nio.ByteBuffer data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</pre>
<div class="block">Creates a new Tensor instance with dtype torch.uint8 with specified shape and data.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>data</code> - Direct buffer with native byte order that contains <code>Tensor.numel(shape)</code>
elements. The buffer is used directly without copying, and changes to its content will
change the tensor.</dd>
<dd><code>shape</code> - Tensor shape</dd>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlobUnsigned(java.nio.ByteBuffer data,
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(java.nio.ByteBuffer data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</pre>
<div class="block">Creates a new Tensor instance with dtype torch.int8 with specified shape and data.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>data</code> - Direct buffer with native byte order that contains <code>Tensor.numel(shape)</code>
elements. The buffer is used directly without copying, and changes to its content will
change the tensor.</dd>
<dd><code>shape</code> - Tensor shape</dd>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(java.nio.ByteBuffer data,
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(java.nio.IntBuffer data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</pre>
<div class="block">Creates a new Tensor instance with dtype torch.int32 with specified shape and data.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>data</code> - Direct buffer with native byte order that contains <code>Tensor.numel(shape)</code>
elements. The buffer is used directly without copying, and changes to its content will
change the tensor.</dd>
<dd><code>shape</code> - Tensor shape</dd>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(java.nio.IntBuffer data,
long[] shape)</pre>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(java.nio.FloatBuffer data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</pre>
<div class="block">Creates a new Tensor instance with dtype torch.float32 with specified shape and data.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>data</code> - Direct buffer with native byte order that contains <code>Tensor.numel(shape)</code>
elements. The buffer is used directly without copying, and changes to its content will
change the tensor.</dd>
<dd><code>shape</code> - Tensor shape</dd>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(java.nio.FloatBuffer data,
long[] shape)</pre>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(java.nio.LongBuffer data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</pre>
<div class="block">Creates a new Tensor instance with dtype torch.int64 with specified shape and data.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>data</code> - Direct buffer with native byte order that contains <code>Tensor.numel(shape)</code>
elements. The buffer is used directly without copying, and changes to its content will
change the tensor.</dd>
<dd><code>shape</code> - Tensor shape</dd>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(java.nio.LongBuffer data,
long[] shape)</pre>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(java.nio.DoubleBuffer data,
long[] shape,
<a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat)</pre>
<div class="block">Creates a new Tensor instance with dtype torch.float64 with specified shape and data.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>data</code> - Direct buffer with native byte order that contains <code>Tensor.numel(shape)</code>
elements. The buffer is used directly without copying, and changes to its content will
change the tensor.</dd>
<dd><code>shape</code> - Tensor shape</dd>
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<pre>public static <a href="../../org/pytorch/Tensor.html" title="class in org.pytorch">Tensor</a> fromBlob(java.nio.DoubleBuffer data,
long[] shape)</pre>
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<pre>public long numel()</pre>
<div class="block">Returns the number of elements in this tensor.</div>
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<pre>public static long numel(long[] shape)</pre>
<div class="block">Calculates the number of elements in a tensor with the specified shape.</div>
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<pre>public long[] shape()</pre>
<div class="block">Returns the shape of this tensor. (The array is a fresh copy.)</div>
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<pre>public <a href="../../org/pytorch/MemoryFormat.html" title="enum in org.pytorch">MemoryFormat</a> memoryFormat()</pre>
<div class="block">Returns the memory format of this tensor.</div>
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<pre>public abstract <a href="../../org/pytorch/DType.html" title="enum in org.pytorch">DType</a> dtype()</pre>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>data type of this tensor.</dd>
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<pre>public byte[] getDataAsByteArray()</pre>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>a Java byte array that contains the tensor data. This may be a copy or reference.</dd>
<dt><span class="throwsLabel">Throws:</span></dt>
<dd><code>java.lang.IllegalStateException</code> - if it is called for a non-int8 tensor.</dd>
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<pre>public byte[] getDataAsUnsignedByteArray()</pre>
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<dt><span class="returnLabel">Returns:</span></dt>
<dd>a Java byte array that contains the tensor data. This may be a copy or reference.</dd>
<dt><span class="throwsLabel">Throws:</span></dt>
<dd><code>java.lang.IllegalStateException</code> - if it is called for a non-uint8 tensor.</dd>
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<pre>public int[] getDataAsIntArray()</pre>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>a Java int array that contains the tensor data. This may be a copy or reference.</dd>
<dt><span class="throwsLabel">Throws:</span></dt>
<dd><code>java.lang.IllegalStateException</code> - if it is called for a non-int32 tensor.</dd>
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<pre>public float[] getDataAsFloatArray()</pre>
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<dt><span class="returnLabel">Returns:</span></dt>
<dd>a Java float array that contains the tensor data. This may be a copy or reference.</dd>
<dt><span class="throwsLabel">Throws:</span></dt>
<dd><code>java.lang.IllegalStateException</code> - if it is called for a non-float32 tensor.</dd>
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<pre>public long[] getDataAsLongArray()</pre>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>a Java long array that contains the tensor data. This may be a copy or reference.</dd>
<dt><span class="throwsLabel">Throws:</span></dt>
<dd><code>java.lang.IllegalStateException</code> - if it is called for a non-int64 tensor.</dd>
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<pre>public double[] getDataAsDoubleArray()</pre>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>a Java double array that contains the tensor data. This may be a copy or reference.</dd>
<dt><span class="throwsLabel">Throws:</span></dt>
<dd><code>java.lang.IllegalStateException</code> - if it is called for a non-float64 tensor.</dd>
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