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master/_sources/beginners_guides/tensor_intro.rst.txt

@@ -151,7 +151,7 @@ An alternative way to migrate data is to use :py:func:`asarray` and specify devi
     x_cpu = tensor.concat((tensor.ones(10, device="cpu"), tensor.zeros(1000, device="cpu")))
 
     # data migration is performed via host
-    x_gpu = tensor.asarray(x_cpu, device="cpu")
+    x_gpu = tensor.asarray(x_cpu, device="gpu")
 
 An advantage of using the function ``asarray`` is that migration from ``usm_ndarray`` instances allocated on different
 devices as well migration from :py:class:`numpy.ndarray` may be accomplished in a single call:

master/beginners_guides/tensor_intro.html

@@ -935,7 +935,7 @@ <h2>Creating an array<a class="headerlink" href="#creating-an-array" title="Perm
 <span class="n">x_cpu</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">tensor</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">),</span> <span class="n">tensor</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">)))</span>
 
 <span class="c1"># data migration is performed via host</span>
-<span class="n">x_gpu</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">x_cpu</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
+<span class="n">x_gpu</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">x_cpu</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;gpu&quot;</span><span class="p">)</span>
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