docstr: ivy.Container instance method variant of ivy.softplus. This method
  simply wraps the function, and so the docstring for ivy.softplus also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      input container.
  beta
      The beta value for the softplus formation. Default: ``None``.
  threshold
      values above this revert to a linear function. Default: ``None``.
  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``.
  complex_mode
      optional specifier for how to handle complex data types. See
      ``ivy.func_wrapper.handle_complex_input`` for more detail.
  out
      optional output container, for writing the result to. It must have a shape
      that the inputs broadcast to.
  
  Returns
  -------
  ret
      a container with the softplus unit function applied element-wise.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([-0.3461, -0.6491]))
  >>> y = x.softplus()
  >>> print(y)
  {
      a: ivy.array([0.535, 0.42])
  }
  
  >>> x = ivy.Container(a=ivy.array([-1., 2., 4.]))
  >>> y = x.softplus(beta=0.5, threshold=2)
  >>> print(y)
  {
      a: ivy.array([0.948, 2.63, 4.25])
  }
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code: def softplus(
      self: ivy.Container,
      /,
      *,
      beta: Optional[Union[int, float, ivy.Container]] = None,
      threshold: Optional[Union[int, float, 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,
      complex_mode: Literal["split", "magnitude", "jax"] = "jax",
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container instance method variant of ivy.softplus. This method
      simply wraps the function, and so the docstring for ivy.softplus also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          input container.
      beta
          The beta value for the softplus formation. Default: ``None``.
      threshold
          values above this revert to a linear function. Default: ``None``.
      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``.
      complex_mode
          optional specifier for how to handle complex data types. See
          ``ivy.func_wrapper.handle_complex_input`` for more detail.
      out
          optional output container, for writing the result to. It must have a shape
          that the inputs broadcast to.
  
      Returns
      -------
      ret
          a container with the softplus unit function applied element-wise.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([-0.3461, -0.6491]))
      >>> y = x.softplus()
      >>> print(y)
      {
          a: ivy.array([0.535, 0.42])
      }
  
      >>> x = ivy.Container(a=ivy.array([-1., 2., 4.]))
      >>> y = x.softplus(beta=0.5, threshold=2)
      >>> print(y)
      {
          a: ivy.array([0.948, 2.63, 4.25])
      }
      """
      return self._static_softplus(
          self,
          beta=beta,
          threshold=threshold,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          complex_mode=complex_mode,
          out=out,
      )
  
imports: import ivy
  from ivy.data_classes.container.base import ContainerBase
  from typing import Optional, Union, List, Dict, Literal
label_desc: Use image node_id 12 for calling the _ContainerWithActivations obj's underlying member method code with example usage: obj.softplus() and returns: self