docstr: ivy.Container static method variant of ivy.conj. This method simply
  wraps the function, and so the docstring for ivy.conj also applies to
  this method with minimal changes.
  
  Parameters
  ----------
  x
      input container.
  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
  -------
  ret
      a container containing output array(s) of the same
      dtype as the input array(s) with the complex conjugates of
      the complex values present in the input array. If x is a
      container of scalar(s) then a container of scalar(s)
      will be returned.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([-1+5j, 0-0j, 1.23j]),
  ...                   b=ivy.array([7.9, 0.31+3.3j, -4.2-5.9j]))
  >>> z = ivy.Container.static_conj(x)
  >>> print(z)
  {
      a: ivy.array([-1-5j, 0+0j, -1.23j]),
      b: ivy.array([7.9, 0.31-3.3j, -4.2+5.9j])
  }
code: def static_conj(
      x: Union[ivy.Container, ivy.Array, ivy.NativeArray],
      /,
      *,
      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.conj. This method simply
      wraps the function, and so the docstring for ivy.conj also applies to
      this method with minimal changes.
  
      Parameters
      ----------
      x
          input container.
      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
      -------
      ret
          a container containing output array(s) of the same
          dtype as the input array(s) with the complex conjugates of
          the complex values present in the input array. If x is a
          container of scalar(s) then a container of scalar(s)
          will be returned.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([-1+5j, 0-0j, 1.23j]),
      ...                   b=ivy.array([7.9, 0.31+3.3j, -4.2-5.9j]))
      >>> z = ivy.Container.static_conj(x)
      >>> print(z)
      {
          a: ivy.array([-1-5j, 0+0j, -1.23j]),
          b: ivy.array([7.9, 0.31-3.3j, -4.2+5.9j])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "conj",
          x,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          out=out,
      )
  
imports: from typing import Optional, Union, List, Dict, Tuple, Sequence
  from numbers import Number
  import ivy
  from ivy.data_classes.container.base import ContainerBase
label_desc: Use image node_id 41 for calling the _ContainerWithElementWiseExperimental obj's underlying member method code with example usage: obj.static_conj() and returns: ContainerBase