class_docstr: Applies local response normalization to the n-dimensional input tensor.
  
  ``x_i \leftarrow \dfrac{x_i}{\left ( k + \dfrac{lpha}{C} \sum_j x_j^2 
  ight )^eta}``.
  
  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
  size: const i32 (Required)
      * Amount of neighboring channels to normalize.
  alpha: const fp32 (Optional)
      * defaults to ``1.0``.
      * Scale factor.
  beta: const fp32 (Optional)
      * defaults to ``0.5``.
      * An exponent.
  k: const fp32 (Optional)
      * defaults to ``1.0``.
      * Additive factor.
  Returns
  -------
  tensor<[n,C,*D], T>
      * same type ans shape as the input tensor ``x``.
  
  Attributes
  ----------
  T: fp32
code: def type_inference(self):
      return self.x.sym_type
  
imports: from ._op_reqs import *
label_desc: Use image node_id 2 for calling the local_response_norm obj's underlying member method code with example usage: obj.type_inference() and returns: self