docstr: ivy.Container instance method variant of ivy.value_is_nan. This
  method simply wraps the function, and so the docstring for
  ivy.value_is_nan also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      input container.
  include_infs
      Whether to include infs and -infs in the check. Default is ``True``.
  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
      Boolean as to whether the input value is a nan or not.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([425]), b=ivy.array([float('nan')]))
  >>> y = x.value_is_nan()
  >>> print(y)
  {
      a: False,
      b: True
  }
  
  >>> x = ivy.Container(a=ivy.array([float('inf')]), b=ivy.array([0]))
  >>> y = x.value_is_nan()
  >>> print(y)
  {
      a: True,
      b: False
  }
  
  >>> x = ivy.Container(a=ivy.array([float('inf')]), b=ivy.array([22]))
  >>> y = x.value_is_nan(include_infs=False)
  >>> print(y)
  {
      a: False,
      b: False
  }
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code: def value_is_nan(
      self: ivy.Container,
      /,
      *,
      include_infs: Union[bool, ivy.Container] = True,
      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 instance method variant of ivy.value_is_nan. This
      method simply wraps the function, and so the docstring for
      ivy.value_is_nan also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          input container.
      include_infs
          Whether to include infs and -infs in the check. Default is ``True``.
      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
          Boolean as to whether the input value is a nan or not.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([425]), b=ivy.array([float('nan')]))
      >>> y = x.value_is_nan()
      >>> print(y)
      {
          a: False,
          b: True
      }
  
      >>> x = ivy.Container(a=ivy.array([float('inf')]), b=ivy.array([0]))
      >>> y = x.value_is_nan()
      >>> print(y)
      {
          a: True,
          b: False
      }
  
      >>> x = ivy.Container(a=ivy.array([float('inf')]), b=ivy.array([22]))
      >>> y = x.value_is_nan(include_infs=False)
      >>> print(y)
      {
          a: False,
          b: False
      }
      """
      return self._static_value_is_nan(
          self,
          include_infs=include_infs,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
      )
  
imports: from numbers import Number
  from typing import Any, Union, List, Dict, Iterable, Optional, Callable
  from ivy.data_classes.container.base import ContainerBase
  import ivy
label_desc: Use image node_id 36 for calling the _ContainerWithGeneral obj's underlying member method code with example usage: obj.value_is_nan() and returns: self