class_docstr: Applies L2 normalization to the n-dimensional input tensor on given ``axes``.
  
  Parameters
  ----------
  x: tensor<[n,C,*D], T> (Required)
      * Input tensor, ``3 <= rank(x) <= 4``.
      * ``*D`` refers to the spatial dimensions, ``1 <= rank(*D) <= 2``.
      * ``n`` is the batch dimension
  axes: const tensor<[K], i32> (Required)
      * Dimensions to perform normalizations.
  epsilon: const fp32 (Optional)
      * Small constant to avoid division by ``0``.
      * Optional, defaults to ``1e-12``.
  
  Returns
  -------
  tensor<[n,C,*D], T>
      * Same type and shape as the input tensor ``x``.
  
  Attributes
  ----------
  T: fp32
code: def __init__(self, **kwargs):
      super(l2_norm, self).__init__(**kwargs)
  
imports: from ._op_reqs import *
label_desc: Use image node_id 1 to create a new l2_norm object from inherited base classes: Operation with example: obj = l2_norm()