docstr: ivy.Container instance method variant of ivy.digamma. This method
  simply wraps the function, and so the docstring for ivy.digamma also
  applies to this method with minimal changes.
  
  Note
  ----
  The Ivy version only accepts real-valued inputs.
  
  Parameters
  ----------
  self
      Input container containing input arrays.
  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
      Alternate output array in which to place the result.
      The default is None.
  
  Returns
  -------
  ret
      container including the digamma function computed element-wise
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([1, 0.5]), b=ivy.array([2.0, 3.0])
  >>> x.digamma()
  {
      a: ivy.array([-0.5772, -1.9635]),
      b: ivy.array([0.4228, 0.9228])
  }
code: def digamma(
      self: ivy.Container,
      /,
      *,
      key_chains: Optional[Union[List[str], Dict[str, str]]] = None,
      to_apply: bool = True,
      prune_unapplied: bool = False,
      map_sequences: bool = False,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container instance method variant of ivy.digamma. This method
      simply wraps the function, and so the docstring for ivy.digamma also
      applies to this method with minimal changes.
  
      Note
      ----
      The Ivy version only accepts real-valued inputs.
  
      Parameters
      ----------
      self
          Input container containing input arrays.
      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
          Alternate output array in which to place the result.
          The default is None.
  
      Returns
      -------
      ret
          container including the digamma function computed element-wise
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([1, 0.5]), b=ivy.array([2.0, 3.0])
      >>> x.digamma()
      {
          a: ivy.array([-0.5772, -1.9635]),
          b: ivy.array([0.4228, 0.9228])
      }
      """
      return self.static_digamma(
          self,
          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 52 for calling the _ContainerWithElementWiseExperimental obj's underlying member method code with example usage: obj.digamma() and returns: self