class_docstr: Apply local response normalization to the n-dimensional input tensor:
  
  .. math::
     x_i \leftarrow \dfrac{x_i}{\left ( k + \dfrac{\alpha}{\text{size}} \sum_j x_j^2 \right )^\beta}
  
  
  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 T (Optional)
      * Scale factor.
      * Default is ``1e-4``.
  beta: const T (Optional)
      * An exponent.
      * Default is ``0.75``.
  k: const T (Optional)
      * Additive factor.
      * Default is ``1.0``.
  
  Returns
  -------
  tensor<[n,C,*D], T>
      * Same type and shape as the input tensor ``x``.
  
  Attributes
  ----------
  T: fp16, fp32
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code: def default_inputs(self):
      return DefaultInputs(
          alpha=1e-4,
          beta=0.75,
          k=1.0,
      )
  
imports: import numpy
  from coremltools.converters.mil.mil import DefaultInputs, InputSpec, Operation, TensorInputType, precondition, types
  from coremltools.converters.mil.mil.operation import VALUE
  from coremltools.converters.mil.mil.ops.defs._op_reqs import register_op
  from coremltools.converters.mil.mil.types.symbolic import any_symbolic
label_desc: Use image node_id 1 for calling the local_response_norm obj's underlying member method code with example usage: obj.default_inputs() and returns: DefaultInputs