Creation#

class ivy.data_classes.container.creation._ContainerWithCreation(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, **kwargs)[source]#

Bases: ContainerBase

_abc_impl = <_abc_data object>#

static _static_arange(start, /, stop=None, step=1, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, dtype=None, device=None, out=None)[source]#

Return type:: Container

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

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

Parameters:

self – input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays.
copy (Optional[bool]) – boolean, indicating whether or not to copy the input. Default: None. (default: None)
dtype (Optional[Union[Dtype, NativeDtype]]) – datatype, optional. Datatype is inferred from the input data. (default: None)
device (Optional[Union[Device, NativeDevice]]) – device on which to place the created array. Default: None. (default: None)
out (Optional[Container]) – optional output array, for writing the result to. It must have a shape that (default: None) the inputs broadcast to.

Return type:

Container

Returns:

ret – An array interpretation of self.

Examples

With ivy.Container as input: >>> x = ivy.Container(a = [(1,2),(3,4),(5,6)], b = ((1,2,3),(4,5,6))) >>> ivy.asarray(x) {

a: ivy.array([[1, 2],
[3, 4], [5, 6]]),

b: ivy.array([[1, 2, 3],
[4, 5, 6]])

}

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

Return type:: Container

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

Return type:: Container

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

Return type:: Container

static _static_eye(n_rows, n_cols=None, /, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, k=0, batch_shape=None, dtype=None, device=None, out=None)[source]#

Return type:: Container

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

Return type:: Container

static _static_full(shape, fill_value, /, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, dtype=None, out=None, device=None)[source]#

Return type:: Container

static _static_full_like(x, /, fill_value, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, dtype=None, device=None, out=None)[source]#

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

Parameters:

self – input container.
fill_value (Union[int, float]) – Scalar fill value
key_chains (Optional[Union[List[str], Dict[str, str]]]) – The key-chains to apply or not apply the method to. Default is None. (default: None)
to_apply (bool) – If True, the method will be applied to key_chains, otherwise key_chains (default: True) will be skipped. Default is True.
prune_unapplied (bool) – Whether to prune key_chains for which the function was not applied. (default: False) Default is False.
map_sequences (bool) – Whether to also map method to sequences (lists, tuples). (default: False) Default is False.
out (Optional[Container]) – optional output container, for writing the result to. It must have a shape (default: None) that the inputs broadcast to.
dtype (Optional[Union[Dtype, NativeDtype]]) – output array data type. If dtype is None, the output array data type (default: None) must be inferred from self. Default: None.
device (Optional[Union[Device, NativeDevice]]) – device on which to place the created array. If device is None, the (default: None) output array device must be inferred from self. Default: None.

Return type:

Container

Returns:

ret – an output container having the same data type as x and whose elements, relative to x, are shifted.

Examples

With ivy.Container input:

>>> x = ivy.Container(a = ivy.array([1,2,3]) ,b = ivy.array([4,5,6]))
>>> fill_value = 10
>>> y = ivy.Container.static_full_like(fill_value)
{
    a: ivy.array([10, 10, 10]),
    b: ivy.array([10, 10, 10])
}

>>> x = ivy.Container(a=ivy.array([1.2, 2.2324, 3.234]),
...                   b=ivy.array([4.123, 5.23, 6.23]))
>>> fill_value = 15.0
>>> y = ivy.Container.static_full_like(fill_value)
>>> print(y)
{
    a: ivy.array([15., 15., 15.]),
    b: ivy.array([15., 15., 15.])
}

static _static_linspace(start, stop, /, num, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, axis=None, endpoint=True, dtype=None, device=None, out=None)[source]#

Return type:: Container

static _static_logspace(start, stop, /, num, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, base=10.0, axis=0, endpoint=True, dtype=None, device=None, out=None)[source]#

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

Parameters:

start (Union[Array, NativeArray, float, Container]) – Container for first value in the range in log space.
stop (Union[Array, NativeArray, float, Container]) – Container for last value in the range in log space.
num (int) – Number of values to generate.
base (float) – The base of the log space. Default is 10.0 (default: 10.0)
axis (int) – Axis along which the operation is performed. Relevant only if values in (default: 0) start or stop containers are array-like. Default is 0.
endpoint (bool) – If True, stop is the last sample. Otherwise, it is not included. Default is (default: True) True.
dtype (Optional[Union[Dtype, NativeDtype]]) – The data type of the output tensor. If None, the dtype of on_value is used (default: None) or if that is None, the dtype of off_value is used, or if that is None, defaults to float32. Default is None.
device (Optional[Union[Device, NativeDevice]]) – device on which to create the array ‘cuda:0’, ‘cuda:1’, ‘cpu’ etc. Default (default: None) is None.
out (Optional[Container]) – optional output array, for writing the result to. It must have a shape that (default: None) the inputs broadcast to. Default is None.

Return type:

Container

Returns:

ret – a container having the same shape as start and filled with tensor of evenly-spaced values in log space.

Examples

>>> import ivy.container.creation.static_logspace as static_logspace
>>> x = ivy.Container(a = 1, b = 0)
>>> y = ivy.Container(a = 4, b = 1)
>>> z = static_logspace(x, y, 4)
{
    a: ivy.array([10.,  100.,  1000., 10000.]),
    b: ivy.array([ 1., 2.15443469, 4.64158883, 10.])
}

static _static_meshgrid(*arrays, sparse=False, indexing='xy', key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

Return type:: Container

static _static_native_array(x, /, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, dtype=None, device=None)[source]#

Return type:: Container

static _static_one_hot(indices, depth, /, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, on_value=None, off_value=None, axis=None, dtype=None, device=None, out=None)[source]#

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

Parameters:

indices (Container) – Indices for where the ones should be scattered [batch_shape, dim]
depth (int) – Scalar defining the depth of the one-hot dimension.
on_value (Optional[Number]) – Value to fill in output when indices[j] = i. If None, defaults to 1. (default: None)
off_value (Optional[Number]) – Value to fill in output when indices[j] != i. If None, defaults to 0. (default: None)
axis (Optional[int]) – Axis to scatter on. The default is -1, a new inner-most axis is created. (default: None)
dtype (Optional[Union[Dtype, NativeDtype]]) – The data type of the output tensor. If None, defaults to the on_value dtype (default: None) or the off_value dtype. If both are None, defaults to float32.
key_chains (Optional[Union[List[str], Dict[str, str]]]) – The key-chains to apply or not apply the method to. Default is None. (default: None)
to_apply (bool) – If True, the method will be applied to key_chains, otherwise key_chains will (default: True) be skipped. Default is True.
prune_unapplied (bool) – Whether to prune key_chains for which the function was not applied. Default (default: False) is False.
map_sequences (bool) – Whether to also map method to sequences (lists, tuples). (default: False) Default is False.

Return type:

Container

Returns:

ret – container with tensors of zeros with the same shape and type as the inputs, unless dtype provided which overrides.

Examples

With ivy.Container inputs:

>>> x = ivy.Container(a=ivy.array([1, 2]),             b=ivy.array([3, 1]), c=ivy.array([2, 3]))
>>> y = 5
>>> z = ivy.Container.static_one_hot(x, y)
>>> print(z)
{
    a: ivy.array([[0., 1., 0., 0., 0.],
                [0., 0., 1., 0., 0.]]),
    b: ivy.array([[0., 0., 0., 1., 0.],
                [0., 1., 0., 0., 0.]]),
    c: ivy.array([[0., 0., 1., 0., 0.],
                [0., 0., 0., 1., 0.]])
}

>>> x = ivy.Container(a=ivy.array([1, 2]),             b=ivy.array([]), c=ivy.native_array([4]))
>>> y = 5
>>> z = ivy.Container.static_one_hot(x, y)
>>> print(z)
{
    a: ivy.array([[0., 1., 0., 0., 0.],
                [0., 0., 1., 0., 0.]]),
    b: ivy.array([], shape=(0, 5)),
    c: ivy.array([[0., 0., 0., 0., 1.]])
}

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

Return type:: Container

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

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

Parameters:

x (Union[Array, NativeArray, Container]) – input array from which to derive the output array shape.
key_chains (Optional[Union[List[str], Dict[str, str]]]) – The key-chains to apply or not apply the method to. Default is None. (default: None)
to_apply (bool) – If True, the method will be applied to key_chains, otherwise key_chains (default: True) will be skipped. Default is True.
prune_unapplied (bool) – Whether to prune key_chains for which the function was not applied. (default: False) Default is False.
map_sequences (bool) – Whether to also map method to sequences (lists, tuples). (default: False) Default is False.
dtype (Optional[Union[Dtype, NativeDtype]]) – output array data type. If dtype is None, the output array data type (default: None) must be inferred from self. Default None.
device (Optional[Union[Device, NativeDevice]]) – device on which to place the created array. If device is None, the (default: None) output array device must be inferred from self. Default: None.
out (Optional[Container]) – optional output array, for writing the result to. It must have a shape that (default: None) the inputs broadcast to.

Return type:

Container

Returns:

ret – a container having the same shape as self and filled with ones.

static _static_tril(x, /, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, k=0, out=None)[source]#

Return type:: Container

static _static_triu(x, /, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, k=0, out=None)[source]#

Return type:: Container

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

Return type:: Container

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

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

Parameters:

x (Union[Array, NativeArray, Container]) – input array or container from which to derive the output container shape.
key_chains (Optional[Union[List[str], Dict[str, str]]]) – The key-chains to apply or not apply the method to. Default is None. (default: None)
to_apply (bool) – If True, the method will be applied to key_chains, otherwise key_chains (default: True) will be skipped. Default is True.
prune_unapplied (bool) – Whether to prune key_chains for which the function was not applied. (default: False) Default is False.
map_sequences (bool) – Whether to also map method to sequences (lists, tuples). (default: False) Default is False.
dtype (Optional[Union[Dtype, NativeDtype]]) – output array data type. If dtype is None, the output container (default: None) data type must be inferred from self. Default None.
device (Optional[Union[Device, NativeDevice]]) – device on which to place the created array. If device is None, the (default: None) output container device must be inferred from self. Default: None.
out (Optional[Container]) – optional output container, for writing the result to. It must have a (default: None) shape that the inputs broadcast to.

Return type:

Container

Returns:

ret – an container having the same shape as x and filled with zeros.

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

Return type:: Container

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

Return type:: Container

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

Return type:: Container

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

Return type:: Container

frombuffer(dtype=<class 'float'>, count=-1, offset=0, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

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

Parameters:

self (Container) – An object that exposes the buffer interface.
dtype (Optional[Union[Dtype, NativeDtype]]) – Data-type of the returned array; default: float. (default: <class 'float'>)
count (Optional[int]) – Number of items to read. -1 means all data in the buffer. (default: -1)
offset (Optional[int]) – Start reading the buffer from this offset (in bytes); default: 0. (default: 0)
key_chains (Optional[Union[List[str], Dict[str, str]]]) – The key-chains to apply or not apply the method to. Default is None. (default: None)
to_apply (bool) – If True, the method will be applied to key_chains, otherwise key_chains will (default: True) be skipped. Default is True.
prune_unapplied (bool) – Whether to prune key_chains for which the function was not applied. Default (default: False) is False.
map_sequences (bool) – Whether to also map method to sequences (lists, tuples). (default: False) Default is False.

Return type:

Container

Returns:

out – 1-dimensional array.

Examples

With ivy.Container inputs:

>>> x = ivy.Container(
...     a = b'\x00\x00\x00\x00\x00\x00\xf0?',
...     b = b'\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\x00@'
... )
>>> y = ivy.Container.static_frombuffer(x)
>>> print(y)
{
    a: ivy.array([1.]),
    b: ivy.array([1., 2.])
}

>>> x = ivy.Container(
...     a = b'\x01\x02\x03\x04',
...     b = b'\x05\x04\x03\x03\x02'
... )
>>> y = ivy.frombuffer(x, dtype=ivy.int8, count=3, offset=1)
>>> print(y)
{
    a: ivy.array([2, 3, 4]),
    b: ivy.array([4, 3, 3])
}

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

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

Parameters:

self (Container) – input container.
fill_value (Union[int, float]) – Scalar fill value
key_chains (Optional[Union[List[str], Dict[str, str]]]) – The key-chains to apply or not apply the method to. Default is None. (default: None)
to_apply (bool) – If True, the method will be applied to key_chains, otherwise key_chains (default: True) will be skipped. Default is True.
prune_unapplied (bool) – Whether to prune key_chains for which the function was not applied. (default: False) Default is False.
map_sequences (bool) – Whether to also map method to sequences (lists, tuples). (default: False) Default is False.
out (Optional[Container]) – optional output container, for writing the result to. It must have a shape (default: None) that the inputs broadcast to.
dtype (Optional[Union[Dtype, NativeDtype]]) – output array data type. If dtype is None, the output array data type (default: None) must be inferred from self. Default: None.
device (Optional[Union[Device, NativeDevice]]) – device on which to place the created array. If device is None, the (default: None) output array device must be inferred from self. Default: None.

Return type:

Container

Returns:

ret – an output container having the same data type as x and whose elements, relative to x, are shifted.

Examples

With ivy.Container input:

>>> x = ivy.Container(a = ivy.array([1,2,3]) ,b = ivy.array([4,5,6]))
>>> fill_value = 10
>>> y = x.full_like(fill_value)
{
    a: ivy.array([10, 10, 10]),
    b: ivy.array([10, 10, 10])
}

>>> x = ivy.Container(a=ivy.array([1.2,2.2324,3.234]),
...                   b=ivy.array([4.123,5.23,6.23]))
>>> fill_value = 15.0
>>> y = x.full_like(fill_value)
>>> print(y)
{
    a: ivy.array([15., 15., 15.]),
    b: ivy.array([15., 15., 15.])
}

linspace(stop, /, num, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, axis=None, endpoint=True, dtype=None, device=None, out=None)[source]#

Return type:: Container

logspace(stop, /, num, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, base=10.0, axis=None, endpoint=True, dtype=None, device=None, out=None)[source]#

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

Parameters:

self (Container) – Container for first value in the range in log space.
stop (Union[Array, NativeArray, float, Container]) – Container for last value in the range in log space.
num (int) – Number of values to generate.
base (float) – The base of the log space. Default is 10.0 (default: 10.0)
axis (Optional[int]) – Axis along which the operation is performed. Relevant only if values in (default: None) start or stop containers are array-like. Default is 0.
endpoint (bool) – If True, stop is the last sample. Otherwise, it is not included. Default is (default: True) True.
dtype (Optional[Union[Dtype, NativeDtype]]) – The data type of the output tensor. If None, the dtype of on_value is used (default: None) or if that is None, the dtype of off_value is used, or if that is None, defaults to float32. Default is None.
device (Optional[Union[Device, NativeDevice]]) – device on which to create the array ‘cuda:0’, ‘cuda:1’, ‘cpu’ etc. Default (default: None) is None.
out (Optional[Container]) – optional output array, for writing the result to. It must have a shape that (default: None) the inputs broadcast to. Default is None.

Return type:

Container

Returns:

ret – a container having the same shape as self and filled with tensor of evenly-spaced values in log space.

Examples

>>> x = ivy.Container(a = 1, b = 0)
>>> y = ivy.Container(a = 4, b = 1)
>>> z = x.logspace(y, 4)
{
    a: ivy.array([10.,  100.,  1000., 10000.]),
    b: ivy.array([ 1., 2.15443469, 4.64158883, 10.])
}

>>> x = ivy.Container(a = 1, b = 0)
>>> y = ivy.Container(a = 4, b = 1)
>>> z = ivy.logspace(x, y, 4)
{
    a: ivy.array([10.,  100.,  1000., 10000.]),
    b: ivy.array([ 1., 2.15443469, 4.64158883, 10.])
}

>>> u = ivy.Container(c = 0, d = 0)
>>> v = ivy.Container(c = 1, d = 2)
>>> x = ivy.Container(a = 1, b = u)
>>> y = ivy.Container(a = 4, b = v)
>>> z = x.logspace(y, 4)
{
    a: ivy.array([10.,  100.,  1000., 10000.]),
    b:  {
            c: ivy.array([ 1., 2.15443469, 4.64158883, 10.])
            d: ivy.array([ 1., 4.64158883, 21.5443469, 100.])
        }
}

meshgrid(*arrays, sparse=False, indexing='xy', key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

Return type:: Container

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

Return type:: Container

one_hot(depth, /, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, on_value=None, off_value=None, axis=None, dtype=None, device=None, out=None)[source]#

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

Parameters:

self (Container) – Indices for where the ones should be scattered [batch_shape, dim]
depth (int) – Scalar defining the depth of the one-hot dimension.
on_value (Optional[Number]) – Value to fill in output when indices[j] == i. If None, defaults to 1. (default: None)
off_value (Optional[Number]) – Value to fill in output when indices[j] != i. If None, defaults to 0. (default: None)
axis (Optional[int]) – Axis to scatter on. The default is -1, a new inner-most axis is created. (default: None)
dtype (Optional[Union[Dtype, NativeDtype]]) – The dtype of the returned tensor. If None, defaults to the on_value dtype (default: None) or the off_value dtype. If both are None, defaults to float32.
key_chains (Optional[Union[List[str], Dict[str, str]]]) – The key-chains to apply or not apply the method to. Default is None. (default: None)
to_apply (bool) – If True, the method will be applied to key_chains, otherwise key_chains will (default: True) be skipped. Default is True.
prune_unapplied (bool) – Whether to prune key_chains for which the function was not applied. Default (default: False) is False.
map_sequences (bool) – Whether to also map method to sequences (lists, tuples). (default: False) Default is False.
out (Optional[Container]) – optional output container, for writing the result to. It must have a (default: None) shape that the inputs broadcast to.

Return type:

Container

Returns:

ret – container with tensors of zeros with the same shape and type as the inputs, unless dtype provided which overrides.

Examples

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([1, 2]),              b=ivy.array([3, 1]), c=ivy.array([2, 3]))
>>> y = 5
>>> z = x.one_hot(y)
>>> print(z)
{
    a: ivy.array([[0., 1., 0., 0., 0.],
                [0., 0., 1., 0., 0.]]),
    b: ivy.array([[0., 0., 0., 1., 0.],
                [0., 1., 0., 0., 0.]]),
    c: ivy.array([[0., 0., 1., 0., 0.],
                [0., 0., 0., 1., 0.]])
}

>>> x = ivy.Container(a=ivy.array([1, 2]),              b=ivy.array([]), c=ivy.native_array([4]))
>>> y = 5
>>> z = x.one_hot(y)
>>> print(z)
{
    a: ivy.array([[0., 1., 0., 0., 0.],
                [0., 0., 1., 0., 0.]]),
    b: ivy.array([], shape=(0, 5)),
    c: ivy.array([[0., 0., 0., 0., 1.]])
}

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

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

Parameters:

self (Container) – input array from which to derive the output array shape.
key_chains (Optional[Union[List[str], Dict[str, str]]]) – The key-chains to apply or not apply the method to. Default is None. (default: None)
to_apply (bool) – If True, the method will be applied to key_chains, otherwise key_chains (default: True) will be skipped. Default is True.
prune_unapplied (bool) – Whether to prune key_chains for which the function was not applied. (default: False) Default is False.
map_sequences (bool) – Whether to also map method to sequences (lists, tuples). (default: False) Default is False.
dtype (Optional[Union[Dtype, NativeDtype]]) – output array data type. If dtype is None, the output array data type (default: None) must be inferred from self. Default None.
device (Optional[Union[Device, NativeDevice]]) – device on which to place the created array. If device is None, the (default: None) output array device must be inferred from self. Default: None.
out (Optional[Container]) – optional output array, for writing the result to. It must have a shape that (default: None) the inputs broadcast to.

Return type:

Container

Returns:

ret – a container having the same shape as self and filled with ones.

static static_frombuffer(buffer, dtype=<class 'float'>, count=-1, offset=0, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

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

Parameters:

buffer (Container) – An object that exposes the buffer interface.
dtype (Optional[Union[Dtype, NativeDtype]]) – Data-type of the returned array; default: float. (default: <class 'float'>)
count (Optional[int]) – Number of items to read. -1 means all data in the buffer. (default: -1)
offset (Optional[int]) – Start reading the buffer from this offset (in bytes); default: 0. (default: 0)
key_chains (Optional[Union[List[str], Dict[str, str]]]) – The key-chains to apply or not apply the method to. Default is None. (default: None)
to_apply (bool) – If True, the method will be applied to key_chains, otherwise key_chains will (default: True) be skipped. Default is True.
prune_unapplied (bool) – Whether to prune key_chains for which the function was not applied. Default (default: False) is False.
map_sequences (bool) – Whether to also map method to sequences (lists, tuples). (default: False) Default is False.

Return type:

Container

Returns:

out – 1-dimensional array.

Examples

With ivy.Container inputs:

>>> x = ivy.Container(
...     a = b'\x00\x00\x00\x00\x00\x00\xf0?',
...     b = b'\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\x00@'
... )
>>> y = ivy.Container.static_frombuffer(x)
>>> print(y)
{
    a: ivy.array([1.]),
    b: ivy.array([1., 2.])
}

>>> x = ivy.Container(
...     a = b'\x01\x02\x03\x04',
...     b = b'\x05\x04\x03\x03\x02'
... )
>>> y = ivy.Container.static_frombuffer(x, dtype=ivy.int8, count=3, offset=1)
>>> print(y)
{
    a: ivy.array([2, 3, 4]),
    b: ivy.array([4, 3, 3])
}

static static_triu_indices(n_rows, n_cols=None, k=0, /, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, device=None, out=None)[source]#

Return type:: Container

tril(key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, k=0, out=None)[source]#

Return type:: Container

triu(key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, k=0, out=None)[source]#

Return type:: Container

triu_indices(n_rows, n_cols=None, k=0, /, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, *, device=None, out=None)[source]#

Return type:: Container

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

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

Parameters:

self (Container) – input array or container from which to derive the output container shape.
key_chains (Optional[Union[List[str], Dict[str, str]]]) – The key-chains to apply or not apply the method to. Default is None. (default: None)
to_apply (bool) – If True, the method will be applied to key_chains, otherwise key_chains (default: True) will be skipped. Default is True.
prune_unapplied (bool) – Whether to prune key_chains for which the function was not applied. (default: False) Default is False.
map_sequences (bool) – Whether to also map method to sequences (lists, tuples). (default: False) Default is False.
dtype (Optional[Union[Dtype, NativeDtype]]) – output array data type. If dtype is None, the output container (default: None) data type must be inferred from self. Default: None.
device (Optional[Union[Device, NativeDevice]]) – device on which to place the created array. If device is None, the (default: None) output container device must be inferred from self. Default: None.
out (Optional[Container]) – optional output container, for writing the result to. It must have a (default: None) shape that the inputs broadcast to.

Return type:

Container

Returns:

ret – an container having the same shape as x and filled with zeros.