docstr: ivy.Container static method variant of ivy.scatter_nd. This method
  simply wraps the function, and so the docstring for ivy.scatter_nd also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  indices
      Index array or container.
  updates
      values to update input tensor with
  shape
      The shape of the result. Default is ``None``, in which case tensor argument
      must be provided.
  reduction
      The reduction method for the scatter, one of 'sum', 'min', 'max'
      or 'replace'
  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``.
  out
      optional output container, for writing the result to. It must have a shape
      that the inputs broadcast to.
  
  Returns
  -------
  ref
      New container of given shape, with the values updated at the indices.
  
  Examples
  --------
  scatter into an empty array
  
  >>> indices = ivy.Container(a=ivy.array([[5],[6],[7]]),
  ...                         b=ivy.array([[2],[3],[4]]))
  >>> updates = ivy.Container(a=ivy.array([50, 60, 70]),
  ...                         b=ivy.array([20, 30, 40]))
  >>> shape = ivy.Container(a=ivy.array([10]),
  ...                       b=ivy.array([10]))
  >>> z = ivy.Container.static_scatter_nd(indices, updates, shape=shape)
  >>> print(z)
  {
      a: ivy.array([0, 0, 0, 0, 0, 50, 60, 70, 0, 0]),
      b: ivy.array([0, 0, 20, 30, 40, 0, 0, 0, 0, 0])
  }
  
  scatter into a container
  
  >>> indices = ivy.Container(a=ivy.array([[5],[6],[7]]),
  ...          b=ivy.array([[2],[3],[4]]))
  >>> updates = ivy.Container(a=ivy.array([50, 60, 70]),
  ...                         b=ivy.array([20, 30, 40]))
  >>> z = ivy.Container(a=ivy.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
  ...                   b=ivy.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))
  >>> ivy.Container.static_scatter_nd(indices, updates,
  ...                                    reduction='replace', out = z)
  >>> print(z)
  {
      a: ivy.array([1, 2, 3, 4, 5, 50, 60, 70, 9, 10]),
      b: ivy.array([1, 2, 20, 30, 40, 6, 7, 8, 9, 10])
  }
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code: def _static_scatter_nd(
      indices: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      updates: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      /,
      shape: Optional[
          Union[ivy.Array, ivy.NativeArray, ivy.Container]
      ] = None,
      *,
      reduction: Union[str, ivy.Container] = "sum",
      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,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container static method variant of ivy.scatter_nd. This method
      simply wraps the function, and so the docstring for ivy.scatter_nd also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      indices
          Index array or container.
      updates
          values to update input tensor with
      shape
          The shape of the result. Default is ``None``, in which case tensor argument
          must be provided.
      reduction
          The reduction method for the scatter, one of 'sum', 'min', 'max'
          or 'replace'
      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``.
      out
          optional output container, for writing the result to. It must have a shape
          that the inputs broadcast to.
  
      Returns
      -------
      ref
          New container of given shape, with the values updated at the indices.
  
      Examples
      --------
      scatter into an empty array
  
      >>> indices = ivy.Container(a=ivy.array([[5],[6],[7]]),
      ...                         b=ivy.array([[2],[3],[4]]))
      >>> updates = ivy.Container(a=ivy.array([50, 60, 70]),
      ...                         b=ivy.array([20, 30, 40]))
      >>> shape = ivy.Container(a=ivy.array([10]),
      ...                       b=ivy.array([10]))
      >>> z = ivy.Container.static_scatter_nd(indices, updates, shape=shape)
      >>> print(z)
      {
          a: ivy.array([0, 0, 0, 0, 0, 50, 60, 70, 0, 0]),
          b: ivy.array([0, 0, 20, 30, 40, 0, 0, 0, 0, 0])
      }
  
      scatter into a container
  
      >>> indices = ivy.Container(a=ivy.array([[5],[6],[7]]),
      ...          b=ivy.array([[2],[3],[4]]))
      >>> updates = ivy.Container(a=ivy.array([50, 60, 70]),
      ...                         b=ivy.array([20, 30, 40]))
      >>> z = ivy.Container(a=ivy.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
      ...                   b=ivy.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))
      >>> ivy.Container.static_scatter_nd(indices, updates,
      ...                                    reduction='replace', out = z)
      >>> print(z)
      {
          a: ivy.array([1, 2, 3, 4, 5, 50, 60, 70, 9, 10]),
          b: ivy.array([1, 2, 20, 30, 40, 6, 7, 8, 9, 10])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "scatter_nd",
          indices,
          updates,
          shape=shape,
          reduction=reduction,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          out=out,
      )
  
imports: from numbers import Number
  from typing import Any, Union, List, Dict, Iterable, Optional, Callable
  from ivy.data_classes.container.base import ContainerBase
  import ivy
label_desc: Use image node_id 25 for calling the _ContainerWithGeneral obj's underlying member method code with example usage: obj._static_scatter_nd(shape) and returns: ContainerBase