docstr: ivy.Container static method variant of ivy.sparsify_tensor. This
  method simply wraps the function, and so the docstring for
  ivy.sparsify_tensor also applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      Input container containing input arrays.
  card
      The number of values to keep in each tensor.
  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 container in which to place the result.
      The default is None.
  
  Returns
  -------
  ret
      container including the sparsified tensor computed element-wise
  
  Examples
  --------
  >>> x = ivy.Container(
          a=ivy.reshape(ivy.arange(100), (10, 10)),
          b=ivy.reshape(ivy.arange(100), (10, 10)),
      )
  >>> ivy.Container.static_sparsify_tensor(x, 10)
      {
          a: (<class ivy.data_classes.array.array.Array> shape=[10, 10]),
          b: (<class ivy.data_classes.array.array.Array> shape=[10, 10])
      }
code: def static_sparsify_tensor(
      x: Union[ivy.Container, ivy.Array, ivy.NativeArray],
      card: Union[int, ivy.Container],
      /,
      *,
      key_chains: Optional[Union[List[str], Dict[str, str]]] = 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.sparsify_tensor. This
      method simply wraps the function, and so the docstring for
      ivy.sparsify_tensor also applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          Input container containing input arrays.
      card
          The number of values to keep in each tensor.
      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 container in which to place the result.
          The default is None.
  
      Returns
      -------
      ret
          container including the sparsified tensor computed element-wise
  
      Examples
      --------
      >>> x = ivy.Container(
              a=ivy.reshape(ivy.arange(100), (10, 10)),
              b=ivy.reshape(ivy.arange(100), (10, 10)),
          )
      >>> ivy.Container.static_sparsify_tensor(x, 10)
          {
              a: (<class ivy.data_classes.array.array.Array> shape=[10, 10]),
              b: (<class ivy.data_classes.array.array.Array> shape=[10, 10])
          }
      """
      return ContainerBase.cont_multi_map_in_function(
          "sparsify_tensor",
          x,
          card,
          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 53 for calling the _ContainerWithElementWiseExperimental obj's underlying member method code with example usage: obj.static_sparsify_tensor() and returns: ContainerBase