docstr: ivy.Container static method variant of ivy.scaled_tanh. This method
  simply wraps the function, and so the docstring for ivy.scaled_tanh
  also applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      input container.
  alpha
      The scaling parameter for the output.
      Determines the amplitude of the tanh function.
      Default: 1.7159
  beta
      The scaling parameter for the input.
      Determines the slope of the tanh function.
      Default: 0.67
  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 with the scaled_tanh function applied.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([8.931, -0.85]), b=ivy.array([1., -0.2])))
  >>> y = ivy.Container._static_scaled_tanh(x)
  >>> y
  {
      a: ivy.array([1.71587813, -0.88367474]),
      b: ivy.array([1.00376701, -0.2285642])
  }
  
  >>> x = ivy.Container(a=ivy.array([8.9, -8.9]), b=ivy.array([3., 33.2]))
  >>> y = ivy.Container._static_scaled_tanh(x, alpha=2, beta=2.5)
  >>> y
  {
      a: ivy.array([2., -2.]),
      b: ivy.array([1.99999881, 2.])
  }
  
  >>> x = ivy.Container(a=ivy.array([0.3, -0.3]), b=ivy.array([33.0, -33.0]))
  >>> y = ivy.Container._static_scaled_tanh(x, alpha=1.5, beta=25)
  >>> y
  {
      a: ivy.array([1.49999905, -1.49999905]),
      b: ivy.array([1.5, -1.5])
  }
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code: def _static_scaled_tanh(
      x: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      /,
      *,
      alpha: Union[float, ivy.Container] = 1.7159,
      beta: Union[float, ivy.Container] = 0.67,
      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.scaled_tanh. This method
      simply wraps the function, and so the docstring for ivy.scaled_tanh
      also applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          input container.
      alpha
          The scaling parameter for the output.
          Determines the amplitude of the tanh function.
          Default: 1.7159
      beta
          The scaling parameter for the input.
          Determines the slope of the tanh function.
          Default: 0.67
      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 with the scaled_tanh function applied.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([8.931, -0.85]), b=ivy.array([1., -0.2])))
      >>> y = ivy.Container._static_scaled_tanh(x)
      >>> y
      {
          a: ivy.array([1.71587813, -0.88367474]),
          b: ivy.array([1.00376701, -0.2285642])
      }
  
      >>> x = ivy.Container(a=ivy.array([8.9, -8.9]), b=ivy.array([3., 33.2]))
      >>> y = ivy.Container._static_scaled_tanh(x, alpha=2, beta=2.5)
      >>> y
      {
          a: ivy.array([2., -2.]),
          b: ivy.array([1.99999881, 2.])
      }
  
      >>> x = ivy.Container(a=ivy.array([0.3, -0.3]), b=ivy.array([33.0, -33.0]))
      >>> y = ivy.Container._static_scaled_tanh(x, alpha=1.5, beta=25)
      >>> y
      {
          a: ivy.array([1.49999905, -1.49999905]),
          b: ivy.array([1.5, -1.5])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "scaled_tanh",
          x,
          alpha=alpha,
          beta=beta,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          out=out,
      )
  
imports: from typing import Union, Optional, List, Dict, Literal
  import ivy
  from ivy.data_classes.container.base import ContainerBase
label_desc: Use image node_id 27 for calling the _ContainerWithActivationExperimental obj's underlying member method code with example usage: obj._static_scaled_tanh() and returns: ContainerBase