class_docstr: Constrains the weights incident to each hidden unit to have unit norm.
  
  Also available via the shortcut function `tf.keras.constraints.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 __call__(self, w):
      return w / (
          backend.epsilon()
          + backend.sqrt(
              math_ops.reduce_sum(
                  math_ops.square(w), axis=self.axis, keepdims=True
              )
          )
      )
  
imports: from tensorflow.python.framework import tensor_shape
  from tensorflow.python.keras import backend
  from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
  from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import while_loop
  from tensorflow.tools.docs import doc_controls
label_desc: Use image node_id 2 for calling the UnitNorm obj's underlying member method code with example usage: obj.__call__(w) and returns: unknown