<li><strong>input</strong> (<em>Variable</em>) – The input variable which is a Tensor or LoDTensor.</li>
<li><strong>dim</strong> (<em>int|None</em>) – The dimension along which the mean is computed. If
<codeclass="xref py py-attr docutils literal"><spanclass="pre">None</span></code>, compute the mean over all elements of <codeclass="xref py py-attr docutils literal"><spanclass="pre">input</span></code>
and return a Tensor variable with a single element, otherwise
must be in the range <spanclass="math">\([-rank(input), rank(input))\)</span>. If
<spanclass="math">\(dim < 0\)</span>, the dimension to reduce is <spanclass="math">\(rank + dim\)</span>.</li>
<li><strong>keep_dim</strong> (<em>bool</em>) – Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the <codeclass="xref py py-attr docutils literal"><spanclass="pre">input</span></code> unless <codeclass="xref py py-attr docutils literal"><spanclass="pre">keep_dim</span></code> is true.</li>
<li><strong>input</strong> (<em>Variable</em>) – The input variable which is a Tensor or LoDTensor.</li>
<li><strong>dim</strong> (<em>int|None</em>) – The dimension along which the mean is computed. If
<codeclass="xref py py-attr docutils literal"><spanclass="pre">None</span></code>, compute the mean over all elements of <codeclass="xref py py-attr docutils literal"><spanclass="pre">input</span></code>
and return a Tensor variable with a single element, otherwise
must be in the range <spanclass="math">\([-rank(input), rank(input))\)</span>. If
<spanclass="math">\(dim < 0\)</span>, the dimension to reduce is <spanclass="math">\(rank + dim\)</span>.</li>
<li><strong>keep_dim</strong> (<em>bool</em>) – Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the <codeclass="xref py py-attr docutils literal"><spanclass="pre">input</span></code> unless <codeclass="xref py py-attr docutils literal"><spanclass="pre">keep_dim</span></code> is true.</li>