class_docstr: Constrains the weights incident to each hidden unit to have unit norm.
  
  Args:
      axis: integer, axis along which to calculate weight norms.
          For instance, in a `Dense` layer the weight matrix
          has shape `(input_dim, output_dim)`,
          set `axis` to `0` to constrain each weight vector
          of length `(input_dim,)`.
          In a `Conv2D` layer with `data_format="channels_last"`,
          the weight tensor has shape
          `(rows, cols, input_depth, output_depth)`,
          set `axis` to `[0, 1, 2]`
          to constrain the weights of each filter tensor of size
          `(rows, cols, input_depth)`.
code: def __init__(self, axis=0):
      self.axis = axis
  
imports: from keras import backend
  from keras import ops
  from keras.api_export import keras_export
label_desc: Use image node_id 1 to create a new UnitNorm object from inherited base classes: Constraint with example: obj = UnitNorm(axis)