x.Name = replacement } } return true }) // Replace identifiers in comments. for _, c := range f.Comments { for _, l := range c.List { for k, v := range replacements { if k == "k" { continue } l.Text = strings.ReplaceAll(l.Text, k, v) } } } out, err := os.Create(*outputFile) if err != nil { log.Fatal(err) } defer out.Close() out.WriteString("// Code generated by generate1024.go. DO NOT EDIT.\n\n") f.Comments = cmap.Filter(f).Comments() err = format.Node(out, fset, f) if err != nil { log.Fatal(err)...

x.Name = replacement } } return true }) // Replace identifiers in comments. for _, c := range f.Comments { for _, l := range c.List { for k, v := range replacements { if k == "k" { continue } l.Text = strings.ReplaceAll(l.Text, k, v) } } } out, err := os.Create(*outputFile) if err != nil { log.Fatal(err) } defer out.Close() out.WriteString("// Code generated by generate1024.go. DO NOT EDIT.\n\n") f.Comments = cmap.Filter(f).Comments() err = format.Node(out, fset, f) if err != nil { log.Fatal(err)...

        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],