public boolean hasNoiseSigma() {
      return gradientNoiseSigmaCase_ == 1;
    }
<pre>
This is a single float value and will be used to add noise to all the
features. Use this field when all features are normalized to have the
same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where
features are normalized to have 0-mean and 1-variance. Learn more about
[normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization).

For best results the recommended value is about 10% - 20% of the standard
deviation of the input feature. Refer to section 3.2 of the SmoothGrad
paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1.

If the distribution is different per feature, set
[feature_noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.feature_noise_sigma]
instead for each feature.
</pre>

<code>float noise_sigma = 1;</code>

@return Whether the noiseSigma field is set.
java-vertexai/proto-google-cloud-vertexai-v1/src/main/java/com/google/cloud/vertexai/api/SmoothGradConfig.java