@java.lang.Override
    public int hashCode() {
      if (memoizedHashCode != 0) {
        return memoizedHashCode;
      }
      int hash = 41;
      hash = (19 * hash) + getDescriptor().hashCode();
      hash = (37 * hash) + NAME_FIELD_NUMBER;
      hash = (53 * hash) + getName().hashCode();
      hash = (37 * hash) + SIGMA_FIELD_NUMBER;
      hash = (53 * hash) + java.lang.Float.floatToIntBits(getSigma());
      hash = (29 * hash) + getUnknownFields().hashCode();
      memoizedHashCode = hash;
      return hash;
    }
<pre>
This represents the standard deviation of the Gaussian kernel that will
be used to add noise to the feature prior to computing gradients. Similar
to [noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.noise_sigma]
but represents the noise added to the current feature. Defaults to 0.1.
</pre>

<code>float sigma = 2;</code>

@return The sigma.
java-vertexai/proto-google-cloud-vertexai-v1/src/main/java/com/google/cloud/vertexai/api/FeatureNoiseSigma.java