Explicit shape comparison for dpnp and numpy outputs f6ab645

Author: github-actions[bot]

pull/2295/.buildinfo

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-  <title>dpnp.dpnp_iface_linearalgebra &mdash; Data Parallel Extension for NumPy 0.18.0dev1+8.g6133ffd3e28 documentation</title>
+  <title>dpnp.dpnp_iface_linearalgebra &mdash; Data Parallel Extension for NumPy 0.18.0dev1+17.gf6ab6450a67 documentation</title>
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@@ -156,18 +156,17 @@ <h1>Source code for dpnp.dpnp_iface_linearalgebra</h1><div class="highlight"><pr
 <span class="c1"># TODO: implement a specific scalar-array kernel</span>
 <span class="k">def</span><span class="w"> </span><span class="nf">_call_multiply</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">outer_calc</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Call multiply function for special cases of scalar-array dots.</span>
-
-<span class="sd">    if `sc` is an scalar and `a` is an array of type float32, we have</span>
-<span class="sd">    dpnp.multiply(a, sc).dtype == dpnp.float32 and</span>
-<span class="sd">    numpy.multiply(a, sc).dtype == dpnp.float32.</span>
-
-<span class="sd">    However, for scalar-array dots such as dot function we have</span>
-<span class="sd">    dpnp.dot(a, sc).dtype == dpnp.float32 while</span>
-<span class="sd">    numpy.dot(a, sc).dtype == dpnp.float64.</span>
-
-<span class="sd">    We need to adjust the behavior of the multiply function when it is</span>
-<span class="sd">    used for special cases of scalar-array dots.</span>
+<span class="sd">    Adjusted multiply function for handling special cases of scalar-array dot</span>
+<span class="sd">    products in linear algebra.</span>
+
+<span class="sd">    `dpnp.multiply` cannot directly be used for calculating scalar-array dots,</span>
+<span class="sd">    because the output dtype of multiply is not the same as the expected dtype</span>
+<span class="sd">    for scalar-array dots. For example, if `sc` is an scalar and `a` is an</span>
+<span class="sd">    array of type `float32`, then `dpnp.multiply(a, sc).dtype == dpnp.float32`</span>
+<span class="sd">    (similar to NumPy). However, for scalar-array dots, such as the dot</span>
+<span class="sd">    function, we need `dpnp.dot(a, sc).dtype == dpnp.float64` to align with</span>
+<span class="sd">    NumPy. This functions adjusts the behavior of `dpnp.multiply` function to</span>
+<span class="sd">    meet this requirement.</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
 

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