docstr: ivy.Container static method variant of ivy.unique_counts. This
  method simply wraps the function, and so the docstring for
  ivy.unique_counts also applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      input container. If ``x`` has more than one dimension, the function must
      flatten ``x`` and return the unique elements of the flattened array.
  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``.
  
  Returns
  -------
  ret
      a namedtuple ``(values, counts)`` whose
  
      - first element must have the field name ``values`` and must be an
      array containing the unique elements of ``x``.
      The array must have the same data type as ``x``.
      - second element must have the field name ``counts`` and must be an array
      containing the number of times each unique element occurs in ``x``.
      The returned array must have same shape as ``values`` and must
      have the default array index data type.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([0., 1., 3. , 2. , 1. , 0.]),
  ...                   b=ivy.array([1,2,1,3,4,1,3]))
  >>> y = ivy.Container.static_unique_counts(x)
  >>> print(y)
  {
      a:[values=ivy.array([0.,1.,2.,3.]),counts=ivy.array([2,2,1,1])],
      b:[values=ivy.array([1,2,3,4]),counts=ivy.array([3,1,2,1])]
  }
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code: def _static_unique_counts(
      x: ivy.Container,
      /,
      *,
      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,
  ) -> ivy.Container:
      """ivy.Container static method variant of ivy.unique_counts. This
      method simply wraps the function, and so the docstring for
      ivy.unique_counts also applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          input container. If ``x`` has more than one dimension, the function must
          flatten ``x`` and return the unique elements of the flattened array.
      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``.
  
      Returns
      -------
      ret
          a namedtuple ``(values, counts)`` whose
  
          - first element must have the field name ``values`` and must be an
          array containing the unique elements of ``x``.
          The array must have the same data type as ``x``.
          - second element must have the field name ``counts`` and must be an array
          containing the number of times each unique element occurs in ``x``.
          The returned array must have same shape as ``values`` and must
          have the default array index data type.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([0., 1., 3. , 2. , 1. , 0.]),
      ...                   b=ivy.array([1,2,1,3,4,1,3]))
      >>> y = ivy.Container.static_unique_counts(x)
      >>> print(y)
      {
          a:[values=ivy.array([0.,1.,2.,3.]),counts=ivy.array([2,2,1,1])],
          b:[values=ivy.array([1,2,3,4]),counts=ivy.array([3,1,2,1])]
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "unique_counts",
          x,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
      )
  
file_path: ivy/ivy/data_classes/container/set.py
imports: from typing import Dict, List, Optional, Union
  from ivy.data_classes.container.base import ContainerBase
  import ivy
label_desc: Use image node_id 3 for calling the _ContainerWithSet obj's underlying member method code with example usage: obj._static_unique_counts() and returns: ContainerBase