class_docstr: Layer that computes the standard deviation across an axis of its inputs.
class_docstr_tok: ["Layer","that","computes","the","standard","deviation","across","an","axis","of","its","inputs","."]
code: def _calculate_mean_feature_standard_deviation(
      self, inputs, batch_axis
  ):
      mean = tf.reduce_mean(inputs, axis=batch_axis, keepdims=True)
      mean_sq_diff = tf.reduce_mean(
          tf.square(inputs - mean), axis=batch_axis, keepdims=True
      )
      mean_stddev = tf.reduce_mean(tf.sqrt(mean_sq_diff), keepdims=True)
      return mean_stddev
  
file_path: graphics/tensorflow_graphics/projects/gan/minibatch_standarddeviation.py
imports: import tensorflow
label_desc: Use image node_id 2 for calling the MiniBatchStandardDeviationBase obj's underlying member method code with example usage: obj._calculate_mean_feature_standard_deviation(inputs, batch_axis) and returns: mean_stddev