docstr: `ivy.Container` static method variant of `is_float_dtype`. This
  method simply wraps this function, so the docstring of `is_float_dtype`
  roughly applies to this method.
  
  Parameters
  ----------
  dtype_in : ivy.Container
      The input to check for float dtype.
  
  key_chains : Optional[Union[List[str], Dict[str, str]]]
      The key chains to use when mapping over the input.
  
  to_apply : bool
      Whether to apply the mapping over the input.
  
  prune_unapplied : bool
      Whether to prune the keys that were not applied.
  
  map_sequences : bool
      Boolean indicating whether to map method
      to sequences (list, tuple). Default is ``False``.
  
  Returns
  -------
  ret : bool
      Boolean indicating whether the input has float dtype.
  
  Examples
  --------
  >>> x = ivy.static_is_float_dtype(ivy.float32)
  >>> print(x)
  True
  
  >>> x = ivy.static_is_float_dtype(ivy.int64)
  >>> print(x)
  False
  
  >>> x = ivy.static_is_float_dtype(ivy.int32)
  >>> print(x)
  False
  
  >>> x = ivy.static_is_float_dtype(ivy.bool)
  >>> print(x)
  False
  
  >>> arr = ivy.array([1.2, 3.2, 4.3], dtype=ivy.float32)
  >>> print(arr.is_float_dtype())
  True
  
  >>> x = ivy.Container(a=ivy.array([0., 1., 2.]), b=ivy.array([3, 4, 5]))
  >>> print(x.a.dtype, x.b.dtype)
  float32 int32
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code: def _static_is_float_dtype(
      dtype_in: 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 `is_float_dtype`. This
      method simply wraps this function, so the docstring of `is_float_dtype`
      roughly applies to this method.
  
      Parameters
      ----------
      dtype_in : ivy.Container
          The input to check for float dtype.
  
      key_chains : Optional[Union[List[str], Dict[str, str]]]
          The key chains to use when mapping over the input.
  
      to_apply : bool
          Whether to apply the mapping over the input.
  
      prune_unapplied : bool
          Whether to prune the keys that were not applied.
  
      map_sequences : bool
          Boolean indicating whether to map method
          to sequences (list, tuple). Default is ``False``.
  
      Returns
      -------
      ret : bool
          Boolean indicating whether the input has float dtype.
  
      Examples
      --------
      >>> x = ivy.static_is_float_dtype(ivy.float32)
      >>> print(x)
      True
  
      >>> x = ivy.static_is_float_dtype(ivy.int64)
      >>> print(x)
      False
  
      >>> x = ivy.static_is_float_dtype(ivy.int32)
      >>> print(x)
      False
  
      >>> x = ivy.static_is_float_dtype(ivy.bool)
      >>> print(x)
      False
  
      >>> arr = ivy.array([1.2, 3.2, 4.3], dtype=ivy.float32)
      >>> print(arr.is_float_dtype())
      True
  
      >>> x = ivy.Container(a=ivy.array([0., 1., 2.]), b=ivy.array([3, 4, 5]))
      >>> print(x.a.dtype, x.b.dtype)
      float32 int32
      """
      return ContainerBase.cont_multi_map_in_function(
          "is_float_dtype",
          dtype_in,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
      )
  
imports: from typing import Optional, Union, List, Dict, Tuple, Callable
  import ivy
  from ivy.data_classes.container.base import ContainerBase
label_desc: Use image node_id 21 for calling the _ContainerWithDataTypes obj's underlying member method code with example usage: obj._static_is_float_dtype(key_chains, to_apply, prune_unapplied, map_sequences) and returns: ContainerBase