docstr: ivy.Container static method variant of ivy.group_norm. This method
  simply wraps the function, and so the docstring for ivy.group_norm also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      Input array of default shape (N, *S, C), where N is the batch dimension,
      *S corresponds to any number of spatial dimensions and
      C corresponds to the channel dimension.
  num_groups
      number of groups to separate the channels into
  offset
      An offset array of size C. If present, will be added
      to the normalized input.
  scale
      A scale array of size C. If present, the scale is
      applied to the normalized input.
  eps
      A small float number to avoid dividing by 0.
  data_format
      The ordering of the dimensions in the input, one of "NSC" or "NCS",
      where N is the batch dimension, S represents any number of spatial
      dimensions and C is the channel dimension. Default is "NSC".
  out
      optional output arrays, for writing the result to.
  
  Returns
  -------
  ret
      The normalized array.
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code: def static_group_norm(
      x: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      num_groups: Union[int, ivy.Container] = 1,
      /,
      *,
      offset: Optional[
          Union[ivy.NativeArray, ivy.Array, ivy.Container]
      ] = None,
      scale: Optional[
          Union[ivy.NativeArray, ivy.Array, ivy.Container]
      ] = None,
      eps: Union[float, ivy.Container] = 1e-5,
      data_format: Union[str, ivy.Container] = "NSC",
      out: Optional[Union[ivy.Array, ivy.Container]] = None,
      key_chains: Optional[
          Union[List[str], Dict[str, str], ivy.Container]
      ] = None,
      to_apply: Union[bool, ivy.Container] = True,
      prune_unapplied: Union[bool, ivy.Container] = False,
      map_sequences: Union[bool, ivy.Container] = False,
  ) -> ivy.Container:
      """ivy.Container static method variant of ivy.group_norm. This method
      simply wraps the function, and so the docstring for ivy.group_norm also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          Input array of default shape (N, *S, C), where N is the batch dimension,
          *S corresponds to any number of spatial dimensions and
          C corresponds to the channel dimension.
      num_groups
          number of groups to separate the channels into
      offset
          An offset array of size C. If present, will be added
          to the normalized input.
      scale
          A scale array of size C. If present, the scale is
          applied to the normalized input.
      eps
          A small float number to avoid dividing by 0.
      data_format
          The ordering of the dimensions in the input, one of "NSC" or "NCS",
          where N is the batch dimension, S represents any number of spatial
          dimensions and C is the channel dimension. Default is "NSC".
      out
          optional output arrays, for writing the result to.
  
      Returns
      -------
      ret
          The normalized array.
      """
      return ContainerBase.cont_multi_map_in_function(
          "group_norm",
          x,
          num_groups,
          scale=scale,
          offset=offset,
          eps=eps,
          out=out,
          data_format=data_format,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
      )
  
file_path: ivy/ivy/data_classes/container/experimental/norms.py
imports: from ivy.data_classes.container.base import ContainerBase
  from typing import Union, List, Dict, Optional, Tuple
  import ivy
label_desc: Use image node_id 9 for calling the _ContainerWithNormsExperimental obj's underlying member method code with example usage: obj.static_group_norm() and returns: ContainerBase