class_docstr: MinMaxNorm weight constraint.
  
  Constrains the weights incident to each hidden unit
  to have the norm between a lower bound and an upper bound.
  
  Also available via the shortcut function `tf.keras.constraints.min_max_norm`.
  
  Args:
    min_value: the minimum norm for the incoming weights.
    max_value: the maximum norm for the incoming weights.
    rate: rate for enforcing the constraint: weights will be
      rescaled to yield
      `(1 - rate) * norm + rate * norm.clip(min_value, max_value)`.
      Effectively, this means that rate=1.0 stands for strict
      enforcement of the constraint, while rate<1.0 means that
      weights will be rescaled at each step to slowly move
      towards a value inside the desired interval.
    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, min_value=0.0, max_value=1.0, rate=1.0, axis=0):
      self.min_value = min_value
      self.max_value = max_value
      self.rate = rate
      self.axis = 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 1 to create a new MinMaxNorm object from inherited base classes: Constraint with example: obj = MinMaxNorm(min_value, max_value, rate, axis)