Data type#

class ivy.data_classes.container.data_type._ContainerWithDataTypes(dict_in=None, queues=None, queue_load_sizes=None, container_combine_method='list_join', queue_timeout=None, print_limit=10, key_length_limit=None, print_indent=4, print_line_spacing=0, ivyh=None, default_key_color='green', keyword_color_dict=None, rebuild_child_containers=False, types_to_iteratively_nest=None, alphabetical_keys=True, dynamic_backend=None, build_callable=False, **kwargs)[source]#

Bases: ContainerBase

_abc_impl = <_abc._abc_data object>#

static _static_astype(x, dtype, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, copy=True, out=None)[source]#

Copy an array to a specified data type irrespective of type- promotion rules.

Casting floating-point NaN and infinity values to integral data types is not specified and is implementation-dependent.

When casting a boolean input array to a numeric data type, a value of True must cast to a numeric value equal to 1, and a value of False must cast to a numeric value equal to 0.

When casting a numeric input array to bool, a value of 0 must cast to False, and a non-zero value must cast to True.

Parameters:

x (Container) – array to cast.
dtype (Union[Dtype, Container]) – desired data type.
copy (Union[bool, Container], default: True) – specifies whether to copy an array when the specified dtype matches the data type of the input array x. If True, a newly allocated array must always be returned. If False and the specified dtype matches the data type of the input array, the input array must be returned; otherwise, a newly allocated must be returned. Default: True.
out (Optional[Container], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Container

Returns:

ret – an array having the specified data type. The returned array must have the same shape as x.

Examples

>>> c = ivy.Container(a=ivy.array([False,True,True]),
...                   b=ivy.array([3.14, 2.718, 1.618]))
>>> ivy.Container.static_astype(c, ivy.int32)
{
    a: ivy.array([0, 1, 1]),
    b: ivy.array([3, 2, 1])
}

static _static_broadcast_arrays(*arrays, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container static method variant of ivy.broadcast_arrays. This method simply wraps the function, and so the docstring for ivy.broadcast_arrays also applies to this method with minimal changes.

Parameters:

arrays (Union[Container, Array, NativeArray]) – an arbitrary number of arrays to-be broadcasted. Each array must have the same shape. And Each array must have the same dtype as its corresponding input array.
key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.
to_apply (Union[bool, Container], default: True) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is True.
prune_unapplied (Union[bool, Container], default: False) – Whether to prune key_chains for which the function was not applied. Default is False.
map_sequences (Union[bool, Container], default: False) – Whether to also map method to sequences (lists, tuples). Default is False.

Return type:

Container

Returns:

ret – A list of containers containing broadcasted arrays

Examples

With ivy.Container inputs:

>>> x1 = ivy.Container(a=ivy.array([1, 2]), b=ivy.array([3, 4]))
>>> x2 = ivy.Container(a=ivy.array([-1.2, 0.4]), b=ivy.array([0, 1]))
>>> y = ivy.Container.static_broadcast_arrays(x1, x2)
>>> print(y)
[{
    a: ivy.array([1, 2]),
    b: ivy.array([3, 4])
}, {
    a: ivy.array([-1.2, 0.4]),
    b: ivy.array([0, 1])
}]

With mixed ivy.Container and ivy.Array inputs:

>>> x1 = ivy.Container(a=ivy.array([4, 5]), b=ivy.array([2, -1]))
>>> x2 = ivy.array([0.2, 3.])
>>> y = ivy.Container.static_broadcast_arrays(x1, x2)
>>> print(y)
[{
    a: ivy.array([4, 5]),
    b: ivy.array([2, -1])
}, {
    a: ivy.array([0.2, 3.]),
    b: ivy.array([0.2, 3.])
}]

static _static_broadcast_to(x, /, shape, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

ivy.Container static method variant of ivy.broadcast_to. This method simply wraps the function, and so the docstring for ivy.broadcast_to also applies to this method with minimal changes.

Parameters:

x (Container) – input array to be broadcasted.
shape (Union[Tuple[int, ...], Container]) – desired shape to be broadcasted to.
out (Optional[Container], default: None) – Optional array to store the broadcasted array.

Return type:

Container

Returns:

ret – Returns the broadcasted array of shape ‘shape’

Examples

With ivy.Container static method:

>>> x = ivy.Container(a=ivy.array([1]),
...                   b=ivy.array([2]))
>>> y = ivy.Container.static_broadcast_to(x,(3, 1))
>>> print(y)
{
    a: ivy.array([1],
                 [1],
                 [1]),
    b: ivy.array([2],
                 [2],
                 [2])
}

static _static_can_cast(from_, to, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container static method variant of ivy.can_cast. This method simply wraps the function, and so the docstring for ivy.can_cast also applies to this method with minimal changes.

Parameters:

from – input container from which to cast.
to (Union[Dtype, Container]) – desired data type.
key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.
to_apply (Union[bool, Container], default: True) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is True.
prune_unapplied (Union[bool, Container], default: False) – Whether to prune key_chains for which the function was not applied. Default is False.
map_sequences (Union[bool, Container], default: False) – Whether to also map method to sequences (lists, tuples). Default is False.

Return type:

Container

Returns:

ret – True if the cast can occur according to type-promotion rules; otherwise, False.

Examples

>>> x = ivy.Container(a=ivy.array([0., 1., 2.]),
...                   b=ivy.array([3, 4, 5]))
>>> print(x.a.dtype, x.b.dtype)
float32 int32

>>> print(ivy.Container.static_can_cast(x, 'int64'))
{
    a: false,
    b: true
}

static _static_default_complex_dtype(*, input=None, complex_dtype=None, as_native=None, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

Return type:: Container

static _static_default_float_dtype(*, input=None, float_dtype=None, as_native=None, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

Return type:: Container

static _static_dtype(x, *, as_native=False, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

Return type:: Container

static _static_finfo(type, /, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container static method variant of ivy.finfo.

Parameters:: type (Container) – input container with leaves to inquire information about.
Return type:: Container
Returns:: ret – container of the same structure as self, with each element as a finfo object for the corresponding dtype of leave in`self`.

Examples

>>> c = ivy.Container(x=ivy.array([-9.5,1.8,-8.9], dtype=ivy.float16),
...                   y=ivy.array([7.6,8.1,1.6], dtype=ivy.float64))
>>> y = ivy.Container.static_finfo(c)
>>> print(y)
{
    x: finfo(resolution=0.001, min=-6.55040e+04, max=6.55040e+04,                    dtype=float16),
    y: finfo(resolution=1e-15, min=-1.7976931348623157e+308,                 max=1.7976931348623157e+308, dtype=float64)
}

static _static_function_supported_dtypes(fn, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

Return type:: Container

static _static_function_unsupported_dtypes(fn, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

Return type:: Container

static _static_iinfo(type, /, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container static method variant of ivy.iinfo. This method simply wraps the function, and so the docstring for ivy.iinfo also applies to this method with minimal changes.

Parameters:

type (Container) – input container with leaves to inquire information about.
key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.
to_apply (Union[bool, Container], default: True) – Boolean indicating whether to apply the method to the key-chains. Default is True.
prune_unapplied (Union[bool, Container], default: False) – Boolean indicating whether to prune the key-chains that were not applied. Default is False.
map_sequences (Union[bool, Container], default: False) – Boolean indicating whether to map method to sequences (list, tuple). Default is False.

Return type:

Container

Returns:

ret – container of the same structure as type, with each element as an iinfo object for the corresponding dtype of leave in`type`.

Examples

>>> c = ivy.Container(x=ivy.array([12,-1800,1084], dtype=ivy.int16),
...                   y=ivy.array([-40000,99,1], dtype=ivy.int32))
>>> y = ivy.Container.static_iinfo(c)
>>> print(y)
{
    x: iinfo(min=-32768, max=32767, dtype=int16),
    y: iinfo(min=-2147483648, max=2147483647, dtype=int32)
}

static _static_is_bool_dtype(dtype_in, /, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

Return type:: Container

static _static_is_complex_dtype(dtype_in, /, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container static method variant of is_complex_dtype. This method simply wraps this function, so the docstring of is_complex_dtype roughly applies to this method.

Parameters:

dtype_in (ivy.Container) – The input to check for complex 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.

Return type:

Container

Returns:

ret (bool) – Boolean indicating whether the input has float dtype.

Examples

>>> x = ivy.Container.static_is_complex_dtype(ivy.complex64)
>>> print(x)
True

>>> x = ivy.Container.static_is_complex_dtype(ivy.int64)
>>> print(x)
False

>>> x = ivy.Container.static_is_complex_dtype(ivy.float32)
>>> print(x)
False

static _static_is_float_dtype(dtype_in, /, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

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.

Return type:

Container

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

static _static_is_int_dtype(dtype_in, /, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

Return type:: Container

static _static_is_uint_dtype(dtype_in, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

Return type:: Container

static _static_result_type(*arrays_and_dtypes, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container static method variant of ivy.result_type. This method simply wraps the function, and so the docstring for ivy.result_type also applies to this method with minimal changes.

Parameters:

arrays_and_dtypes (Container) – an arbitrary number of input arrays and/or dtypes.
key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.
to_apply (Union[bool, Container], default: True) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is True.
prune_unapplied (Union[bool, Container], default: False) – Whether to prune key_chains for which the function was not applied. Default is False.
map_sequences (Union[bool, Container], default: False) – Whether to also map method to sequences (lists, tuples). Default is False.

Return type:

Container

Returns:

ret – the dtype resulting from an operation involving the input arrays and dtypes.

Examples

>>> x = ivy.Container(a = ivy.array([0, 1, 2]),
...                   b = ivy.array([3., 4., 5.]))
>>> print(x.a.dtype, x.b.dtype)
int32 float32

>>> print(ivy.Container.static_result_type(x, ivy.float64))
{
    a: float64,
    b: float32
}

astype(dtype, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, copy=True, out=None)[source]#

Copy an array to a specified data type irrespective of type- promotion rules.

Casting floating-point NaN and infinity values to integral data types is not specified and is implementation-dependent.

When casting a boolean input array to a numeric data type, a value of True must cast to a numeric value equal to 1, and a value of False must cast to a numeric value equal to 0.

When casting a numeric input array to bool, a value of 0 must cast to False, and a non-zero value must cast to True.

Parameters:

self (Container) – array to cast.
dtype (Union[Dtype, Container]) – desired data type.
copy (Union[bool, Container], default: True) – specifies whether to copy an array when the specified dtype matches the data type of the input array x. If True, a newly allocated array must always be returned. If False and the specified dtype matches the data type of the input array, the input array must be returned; otherwise, a newly allocated must be returned. Default: True.
out (Optional[Container], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Container

Returns:

ret – an array having the specified data type. The returned array must have the same shape as x.

Examples

Using ivy.Container instance method:

>>> x = ivy.Container(a=ivy.array([False,True,True]),
...                   b=ivy.array([3.14, 2.718, 1.618]))
>>> print(x.astype(ivy.int32))
{
    a: ivy.array([0, 1, 1]),
    b: ivy.array([3, 2, 1])
}

broadcast_arrays(*arrays, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container instance method variant of ivy.broadcast_arrays. This method simply wraps the function, and so the docstring for ivy.broadcast_arrays also applies to this method with minimal changes.

Parameters:

self (Container) – A container to be broadcatsed against other input arrays.
arrays (Union[Container, Array, NativeArray]) – an arbitrary number of containers having arrays to-be broadcasted. Each array must have the same shape. Each array must have the same dtype as its corresponding input array.
key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.
to_apply (Union[bool, Container], default: True) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is True.
prune_unapplied (Union[bool, Container], default: False) – Whether to prune key_chains for which the function was not applied. Default is False.
map_sequences (Union[bool, Container], default: False) – Whether to also map method to sequences (lists, tuples). Default is False.

Return type:

Container

Examples

With ivy.Container inputs:

>>> x1 = ivy.Container(a=ivy.array([1, 2]), b=ivy.array([3, 4]))
>>> x2 = ivy.Container(a=ivy.array([-1.2, 0.4]), b=ivy.array([0, 1]))
>>> y = x1.broadcast_arrays(x2)
>>> print(y)
[{
    a: ivy.array([1, 2]),
    b: ivy.array([3, 4])
}, {
    a: ivy.array([-1.2, 0.4]),
    b: ivy.array([0, 1])
}]

With mixed ivy.Container and ivy.Array inputs:

>>> x1 = ivy.Container(a=ivy.array([4, 5]), b=ivy.array([2, -1]))
>>> x2 = ivy.zeros(2)
>>> y = x1.broadcast_arrays(x2)
>>> print(y)
[{
    a: ivy.array([4, 5]),
    b: ivy.array([2, -1])
}, {
    a: ivy.array([0., 0.]),
    b: ivy.array([0., 0.])
}]

broadcast_to(shape, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

ivy.Container instance method variant of ivy.broadcast_to. This method simply wraps the function, and so the docstring for ivy.broadcast_to also applies to this method with minimal changes.

Parameters:

self (Container) – input array to be broadcasted.
shape (Union[Tuple[int, ...], Container]) – desired shape to be broadcasted to.
out (Optional[Container], default: None) – Optional array to store the broadcasted array.

Return type:

Container

Returns:

ret – Returns the broadcasted array of shape ‘shape’

Examples

With ivy.Container instance method:

>>> x = ivy.Container(a=ivy.array([0, 0.5]),
...                   b=ivy.array([4, 5]))
>>> y = x.broadcast_to((3,2))
>>> print(y)
{
    a: ivy.array([[0., 0.5],
                  [0., 0.5],
                  [0., 0.5]]),
    b: ivy.array([[4, 5],
                  [4, 5],
                  [4, 5]])
}

can_cast(to, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container instance method variant of ivy.can_cast. This method simply wraps the function, and so the docstring for ivy.can_cast also applies to this method with minimal changes.

Parameters:

self (Container) – input container from which to cast.
to (Union[Dtype, Container]) – desired data type.
key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.
to_apply (Union[bool, Container], default: True) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is True.
prune_unapplied (Union[bool, Container], default: False) – Whether to prune key_chains for which the function was not applied. Default is False.
map_sequences (Union[bool, Container], default: False) – Whether to also map method to sequences (lists, tuples). Default is False.

Return type:

Container

Returns:

ret – True if the cast can occur according to type-promotion rules; otherwise, False.

Examples

>>> x = ivy.Container(a=ivy.array([0., 1., 2.]),
...                   b=ivy.array([3, 4, 5]))
>>> print(x.a.dtype, x.b.dtype)
float32 int32

>>> print(x.can_cast('int64'))
{
    a: False,
    b: True
}

dtype(*, as_native=False, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

Return type:: Container

Examples

>>> x = ivy.Container(a=ivy.array([1, 2, 3]), b=ivy.array([2, 3, 4]))
>>> y = x.dtype()
>>> print(y)
{
    a: int32,
    b: int32
}

finfo(*, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container instance method variant of ivy.finfo.

Parameters:: self (Container) – input container with leaves to inquire information about.
Return type:: Container
Returns:: ret – container of the same structure as self, with each element as a finfo object for the corresponding dtype of leave in`self`.

Examples

>>> c = ivy.Container(x=ivy.array([-9.5,1.8,-8.9], dtype=ivy.float16),
...                   y=ivy.array([7.6,8.1,1.6], dtype=ivy.float64))
>>> print(c.finfo())
{
    x: finfo(resolution=0.001, min=-6.55040e+04, max=6.55040e+04,                    dtype=float16),
    y: finfo(resolution=1e-15, min=-1.7976931348623157e+308,                 max=1.7976931348623157e+308, dtype=float64)
}

iinfo(key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container instance method variant of ivy.iinfo. This method simply wraps the function, and so the docstring for ivy.iinfo also applies to this method with minimal changes.

Parameters:

self (Container) – input container with leaves to inquire information about.
key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.
to_apply (Union[bool, Container], default: True) – Boolean indicating whether to apply the method to the key-chains. Default is True.
prune_unapplied (Union[bool, Container], default: False) – Boolean indicating whether to prune the key-chains that were not applied. Default is False.
map_sequences (Union[bool, Container], default: False) – Boolean indicating whether to map method to sequences (list, tuple). Default is False.

Return type:

Container

Returns:

ret – container of the same structure as self, with each element as an iinfo object for the corresponding dtype of leave in`self`.

Examples

>>> c = ivy.Container(x=ivy.array([-9,1800,89], dtype=ivy.int16),
...                   y=ivy.array([76,-81,16], dtype=ivy.int32))
>>> c.iinfo()
{
    x: iinfo(min=-32768, max=32767, dtype=int16),
    y: iinfo(min=-2147483648, max=2147483647, dtype=int32)
}

>>> c = ivy.Container(x=ivy.array([-12,123,4], dtype=ivy.int8),
...                   y=ivy.array([76,-81,16], dtype=ivy.int16))
>>> c.iinfo()
{
    x: iinfo(min=-128, max=127, dtype=int8),
    y: iinfo(min=-32768, max=32767, dtype=int16)
}

is_bool_dtype(key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

Return type:: Container

is_complex_dtype(key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container instance method variant of ivy.is_complex_dtype. This method simply wraps the function, and so the docstring for ivy.is_complex_dtype also applies to this method with minimal changes.

Parameters:

self (ivy.Container) – The ivy.Container instance to call ivy.is_complex_dtype on.
key_chains (Union[List[str], Dict[str, str]]) – The key-chains to apply or not apply the method to. Default is None.
to_apply (bool) – Boolean indicating whether to apply the method to the key-chains. Default is False.
prune_unapplied (bool) – Boolean indicating whether to prune the key-chains that were not applied. Default is False.
map_sequences (bool) – Boolean indicating whether to map method to sequences (list, tuple). Default is False.

Return type:

Container

Returns:

ret (bool) – Boolean of whether the input is of a complex dtype.

Examples

>>> x = ivy.is_complex_dtype(ivy.complex64)
>>> print(x)
True

>>> x = ivy.is_complex_dtype(ivy.int64)
>>> print(x)
False

>>> x = ivy.is_complex_dtype(ivy.float32)
>>> print(x)
False

is_float_dtype(key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container instance method variant of ivy.is_float_dtype. This method simply wraps the function, and so the docstring for ivy.is_float_dtype also applies to this method with minimal changes.

Parameters:

self (ivy.Container) – The ivy.Container instance to call ivy.is_float_dtype on.
key_chains (Union[List[str], Dict[str, str]]) – The key-chains to apply or not apply the method to. Default is None.
to_apply (bool) – Boolean indicating whether to apply the method to the key-chains. Default is False.
prune_unapplied (bool) – Boolean indicating whether to prune the key-chains that were not applied. Default is False.
map_sequences (bool) – Boolean indicating whether to map method to sequences (list, tuple). Default is False.

Return type:

Container

Returns:

ret (bool) – Boolean of whether the input is of a float dtype.

Examples

>>> x = ivy.is_float_dtype(ivy.float32)
>>> print(x)
True

>>> x = ivy.is_float_dtype(ivy.int64)
>>> print(x)
False

>>> x = ivy.is_float_dtype(ivy.int32)
>>> print(x)
False

>>> x = ivy.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

is_int_dtype(key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

Return type:: Container

is_uint_dtype(key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

Return type:: Container

result_type(*arrays_and_dtypes, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container instance method variant of ivy.result_type. This method simply wraps the function, and so the docstring for ivy.result_type also applies to this method with minimal changes.

Parameters:

self (Container) – input container from which to cast.
arrays_and_dtypes (Container) – an arbitrary number of input arrays and/or dtypes.
key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.
to_apply (Union[bool, Container], default: True) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is True.
prune_unapplied (Union[bool, Container], default: False) – Whether to prune key_chains for which the function was not applied. Default is False.
map_sequences (Union[bool, Container], default: False) – Whether to also map method to sequences (lists, tuples). Default is False.

Return type:

Container

Returns:

ret – the dtype resulting from an operation involving the input arrays and dtypes.

Examples

>>> x = ivy.Container(a = ivy.array([3, 3, 3]))
>>> print(x.a.dtype)
int32

>>> y = ivy.Container(b = ivy.float64)
>>> print(x.result_type(y))
{
    a: {
        b: float64
    }
}

This should have hopefully given you an overview of the data_type submodule, if you have any questions, please feel free to reach out on our discord!