docstr: ivy.Container static method variant of ivy.instance_norm. This
  method simply wraps the function, and so the docstring for
  ivy.instance_norm also applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      Input array of shape default (N, *S, C), where N is the batch dimension,
      *S corresponds to any number of spatial dimensions and
       C corresponds to the channel dimension.
  mean
      Mean array of size C used for input's normalization.
  variance
      Variance array of size C used for input's normalization.
  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.
  training
      If true, calculate and use the mean and variance of `x`.
       Otherwise, use the provided `mean` and `variance`.
  eps
      A small float number to avoid dividing by 0.
  momentum
       the value used for the running_mean and running_var computation.
        Default value is 0.1.
  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.
  key_chains
      The key-chains to apply or not apply the method to. Default is ``None``.
  to_apply
      If True, the method will be applied to key_chains, otherwise key_chains
      will be skipped. Default is ``True``.
  prune_unapplied
      Whether to prune key_chains for which the function was not applied.
      Default is ``False``.
  map_sequences
      Whether to also map method to sequences (lists, tuples).
      Default is ``False``.
  
  Returns
  -------
  ret
       Tuple of containers
        containing the normalized input, running mean, and running variance.
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code: def static_instance_norm(
      x: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      mean: Union[ivy.NativeArray, ivy.Array, ivy.Container],
      variance: Union[ivy.NativeArray, ivy.Array, ivy.Container],
      /,
      *,
      offset: Optional[
          Union[ivy.NativeArray, ivy.Array, ivy.Container]
      ] = None,
      scale: Optional[
          Union[ivy.NativeArray, ivy.Array, ivy.Container]
      ] = None,
      training: Union[bool, ivy.Container] = False,
      eps: Union[float, ivy.Container] = 1e-5,
      momentum: Union[float, ivy.Container] = 1e-1,
      data_format: Union[str, ivy.Container] = "NSC",
      out: Optional[
          Tuple[
              Union[ivy.Array, ivy.Container],
              Union[ivy.Array, ivy.Container],
              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,
  ) -> Tuple[ivy.Container, ivy.Container, ivy.Container]:
      """ivy.Container static method variant of ivy.instance_norm. This
      method simply wraps the function, and so the docstring for
      ivy.instance_norm also applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          Input array of shape default (N, *S, C), where N is the batch dimension,
          *S corresponds to any number of spatial dimensions and
           C corresponds to the channel dimension.
      mean
          Mean array of size C used for input's normalization.
      variance
          Variance array of size C used for input's normalization.
      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.
      training
          If true, calculate and use the mean and variance of `x`.
           Otherwise, use the provided `mean` and `variance`.
      eps
          A small float number to avoid dividing by 0.
      momentum
           the value used for the running_mean and running_var computation.
            Default value is 0.1.
      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.
      key_chains
          The key-chains to apply or not apply the method to. Default is ``None``.
      to_apply
          If True, the method will be applied to key_chains, otherwise key_chains
          will be skipped. Default is ``True``.
      prune_unapplied
          Whether to prune key_chains for which the function was not applied.
          Default is ``False``.
      map_sequences
          Whether to also map method to sequences (lists, tuples).
          Default is ``False``.
  
      Returns
      -------
      ret
           Tuple of containers
            containing the normalized input, running mean, and running variance.
      """
      return ContainerBase.cont_multi_map_in_function(
          "instance_norm",
          x,
          mean,
          variance,
          scale=scale,
          offset=offset,
          training=training,
          eps=eps,
          momentum=momentum,
          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 7 for calling the _ContainerWithNormsExperimental obj's underlying member method code with example usage: obj.static_instance_norm() and returns: ContainerBase