class_docstr: MaxNorm weight constraint.
  
  Constrains the weights incident to each hidden unit
  to have a norm less than or equal to a desired value.
  
  Also available via the shortcut function `tf.keras.constraints.max_norm`.
  
  Args:
    max_value: the maximum norm value for the incoming weights.
    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 get_config(self):
      return {"max_value": self.max_value, "axis": self.axis}
  
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 3 for calling the MaxNorm obj's underlying member method code with example usage: obj.get_config() and returns: dict