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Container#

class ivy.data_classes.container.container.Container(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: _ContainerWithActivations, _ContainerWithConversions, _ContainerWithCreation, _ContainerWithDataTypes, _ContainerWithDevice, _ContainerWithElementwise, _ContainerWithGeneral, _ContainerWithGradients, _ContainerWithImage, _ContainerWithLayers, _ContainerWithLinearAlgebra, _ContainerWithLosses, _ContainerWithManipulation, _ContainerWithNorms, _ContainerWithRandom, _ContainerWithSearching, _ContainerWithSet, _ContainerWithSorting, _ContainerWithStatistical, _ContainerWithUtility, _ContainerWithActivationExperimental, _ContainerWithConversionExperimental, _ContainerWithCreationExperimental, _ContainerWithData_typeExperimental, _ContainerWithDeviceExperimental, _ContainerWithElementWiseExperimental, _ContainerWithGeneralExperimental, _ContainerWithGradientsExperimental, _ContainerWithImageExperimental, _ContainerWithLayersExperimental, _ContainerWithLinearAlgebraExperimental, _ContainerWithManipulationExperimental, _ContainerWithNormsExperimental, _ContainerWithRandomExperimental, _ContainerWithSearchingExperimental, _ContainerWithSetExperimental, _ContainerWithSortingExperimental, _ContainerWithStatisticalExperimental, _ContainerWithUtilityExperimental, _ContainerWithLossesExperimental

__abs__()[source]#

ivy.Container special method for the abs operator, calling operator.abs for each of the corresponding leaves of the two containers.

Parameters:

self – input Container. Should have leaves with numeric data type.

Returns:

ret – A container containing the element-wise results.

Examples

With ivy.Container instances:

>>> x = ivy.Container(a=ivy.array([1, -2, 3]),
...                    b=ivy.array([-1, 0, 5]))
>>> y = abs(x)
>>> print(y)
{
    a: ivy.array([1, 2, 3]),
    b: ivy.array([1, 0, 5])
}
__add__(other)[source]#

ivy.Container special method for the add operator, calling operator.add for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input container. Should have a numeric data type.

  • other – second input array or container. Must be compatible with self (see broadcasting). Should have a numeric data type.

Returns:

ret – a container containing the element-wise sums. The returned array must have a data type determined by type-promotion.

Examples

With Number instances at the leaves:

>>> x = ivy.Container(a=1, b=2)
>>> y = ivy.Container(a=3, b=4)
>>> z = x + y
>>> print(z)
{
    a: 4,
    b: 6
}

With ivy.Array instances at the leaves:

>>> x = ivy.Container(a=ivy.array([1, 2, 3]),
...                   b=ivy.array([2, 3, 4]))
>>> y = ivy.Container(a=ivy.array([4, 5, 6]),
...                   b=ivy.array([5, 6, 7]))
>>> z = x + y
>>> print(z)
{
    a: ivy.array([5, 7, 9]),
    b: ivy.array([7, 9, 11])
}

With a mix of ivy.Container and ivy.Array instances:

>>> x = ivy.Container(a=ivy.array([[4.], [5.], [6.]]),
...                   b=ivy.array([[5.], [6.], [7.]]))
>>> y = ivy.array([[1.1, 2.3, -3.6]])
>>> z = x + y
>>> print(z)
{
    a: ivy.array([[5.1, 6.3, 0.4],
                  [6.1, 7.3, 1.4],
                  [7.1, 8.3, 2.4]]),
    b: ivy.array([[6.1, 7.3, 1.4],
                  [7.1, 8.3, 2.4],
                  [8.1, 9.3, 3.4]])
}
__eq__(other)[source]#

ivy.Container special method for the equal operator, calling operator.eq for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input Container. May have any data type.

  • other – second input Container. Must be compatible with x1 (with Broadcasting). May have any data type.

Returns:

ret – A container containing the element-wise results. Any returned array inside must have a data type of bool.

Examples

With ivy.Container instances:

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

>>> x1 = ivy.Container(a=ivy.array([1.0, 2.0, 3.0]),
...                    b=ivy.array([1, 4, 5]))
>>> x2 = ivy.Container(a=ivy.array([1, 3, 3.0]),
...                    b=ivy.array([1.0, 4.0, 5.0]))
>>> y = x1 == x2
>>> print(y)
{
    a: ivy.array([True, False, True]),
    b: ivy.array([True, True, True])
}

>>> x1 = ivy.Container(a=ivy.array([1.0, 2.0, 3.0]),
...                    b=ivy.array([1, 4, 5]))
>>> x2 = ivy.Container(a=ivy.array([1, 2, 3.0]),
...                    b=ivy.array([1.0, 4.0, 5.0]))
>>> y = x1 == x2
>>> print(y)
{
    a: ivy.array([True, True, True]),
    b: ivy.array([True, True, True])
}
__ge__(other)[source]#

ivy.Container special method for the greater_equal operator, calling operator.ge for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input Container. May have any data type.

  • other – second input Container. Must be compatible with x1 (with Broadcasting). May have any data type.

Returns:

ret – A container containing the element-wise results. Any returned array inside must have a data type of bool.

Examples

With ivy.Container instances:

>>> x = ivy.Container(a=ivy.array([4, 5, 6]),b=ivy.array([2, 3, 4]))
>>> y = ivy.Container(a=ivy.array([1, 5, 3]),b=ivy.array([5, 3, 7]))
>>> z = x >= y
>>> print(z)
{
    a:ivy.array([True,True,True]),
    b:ivy.array([False,True,False])
}
__gt__(other)[source]#

ivy.Container special method for the greater operator, calling operator.gt for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input Container. May have any data type.

  • other – second input Container. Must be compatible with x1 (with Broadcasting). May have any data type.

Returns:

ret – A container containing the element-wise results. Any returned array inside must have a data type of bool.

Examples

With ivy.Container instances:

>>> x = ivy.Container(a=ivy.array([4, 5, 6]),b=ivy.array([2, 3, 4]))
>>> y = ivy.Container(a=ivy.array([1, 5, 3]),b=ivy.array([5, 3, 7]))
>>> z = x > y
>>> print(z)
{
    a:ivy.array([True,False,True]),
    b:ivy.array([False,False,False])
}
__init__(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]#

Initialize container object from input dict representation.

Parameters:

  • dict_in (default: None) – the dictionary the container should wrap around. Default is None.

  • queues (default: None) – Sequence of multiprocessing queues, each of which returns containers. This enables the current container to be passed around asynchronously while waiting for data. Default is None.

  • queue_load_sizes (default: None) – Size of leading dimension of the containers returned by each queue. Default is None.

  • container_combine_method (default: 'list_join') – The method to use for combining containers arriving from different queues. Default is ivy.Container.cont_list_join

  • queue_timeout (default: None) – The timeout when waiting for containers to arrive from the queues. Default is global.

  • print_limit (default: 10) – The total array size limit when printing the container. Default is 10.

  • key_length_limit (default: None) – The maximum key length when printing the container. Default is None.

  • print_indent (default: 4) – The number of whitespaces to use for indenting when printing the container. Default is 4.

  • print_line_spacing (default: 0) – The number of extra newlines to use between keys when printing the container. Default is 0.

  • ivyh (default: None) – Handle to ivy module to use for the calculations. Default is None, which results in the global ivy.

  • default_key_color (default: 'green') – The default key color for printing the container to the terminal. Default is ‘green’.

  • rebuild_child_containers (default: False) – Whether to rebuild container found in dict_in with these constructor params. Default is False, in which case the original container are kept as are.

  • build_callable – Whether to treat functions encountered at leaf nodes as further instructions to build the container

  • types_to_iteratively_nest (default: None) – The data types to nest iteratively in the dict structure, each type must be iterable. Default is None.

  • alphabetical_keys (default: True) – Whether to sort the container keys alphabetically, or preserve the dict order. Default is True.

  • kwargs – keyword arguments for dict creation. Default is None.

__le__(other)[source]#

ivy.Container special method for the less_equal operator, calling operator.le for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input Container. May have any data type.

  • other – second input Container. Must be compatible with x1 (with Broadcasting). May have any data type.

Returns:

ret – A container containing the element-wise results. Any returned array inside must have a data type of bool.

Examples

With ivy.Container instances:

>>> x = ivy.Container(a=ivy.array([4, 5, 6]),b=ivy.array([2, 3, 4]))
>>> y = ivy.Container(a=ivy.array([1, 5, 3]),b=ivy.array([5, 3, 7]))
>>> z = x <= y
>>> print(z)
{
    a: ivy.array([False, True, False]),
    b: ivy.array([True, True, True])
}
__lt__(other)[source]#

ivy.Container special method for the less operator, calling operator.lt for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input Container. May have any data type.

  • other – second input Container. Must be compatible with x1 (with Broadcasting). May have any data type.

Returns:

ret – A container containing the element-wise results. Any returned array inside must have a data type of bool.

Examples

With ivy.Container instances:

>>> x = ivy.Container(a=ivy.array([4, 5, 6]),b=ivy.array([2, 3, 4]))
>>> y = ivy.Container(a=ivy.array([1, 5, 3]),b=ivy.array([5, 3, 7]))
>>> z = x < y
>>> print(z)
{
    a: ivy.array([False, False, False]),
    b: ivy.array([True, False, True])
}
__ne__(other)[source]#

ivy.Container special method for the not_equal operator, calling operator.ne for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input Container. May have any data type.

  • other – second input Container. Must be compatible with x1 (with Broadcasting). May have any data type.

Returns:

ret – A container containing the element-wise results. Any returned array inside must have a data type of bool.

Examples

With ivy.Container instances:

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

>>> x1 = ivy.Container(a=ivy.array([1.0, 2.0, 3.0]),
...                    b=ivy.array([1, 4, 5]))
>>> x2 = ivy.Container(a=ivy.array([1, 3, 3.0]),
...                    b=ivy.array([1.0, 4.0, 5.0]))
>>> y = x1 != x2
>>> print(y)
{
    a: ivy.array([False, True, False]),
    b: ivy.array([False, False, False])
}

>>> x1 = ivy.Container(a=ivy.array([1.0, 2.0, 3.0]),
...                    b=ivy.array([1, 4, 5]))
>>> x2 = ivy.Container(a=ivy.array([1, 2, 3.0]),
...                    b=ivy.array([1.0, 4.0, 5.0]))
>>> y = x1 != x2
>>> print(y)
{
    a: ivy.array([False, False, False]),
    b: ivy.array([False, False, False])
}
__pow__(power)[source]#

ivy.Container special method for the power operator, calling operator.pow for each of the corresponding leaves of the two containers.

Parameters:

  • self – input container. Should have a numeric data type.

  • power – input array or container of powers. Must be compatible with self (see broadcasting). Should have a numeric data type.

Returns:

ret – a container containing the element-wise sums. The returned array must have a data type determined by type-promotion.

Examples

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([0, 1]), b=ivy.array([2, 3]))
>>> y = x ** 2
>>> print(y)
{
    a: ivy.array([0, 1]),
    b: ivy.array([4, 9])
}
>>> x = ivy.Container(a=ivy.array([0, 1.2]), b=ivy.array([2.2, 3.]))
>>> y = x ** 3.1
>>> print(y)
{
    a: ivy.array([0., 1.75979435]),
    b: ivy.array([11.52153397, 30.13532257])
}
__radd__(other)[source]#

ivy.Container reverse special method for the add operator, calling operator.add for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input container. Should have a numeric data type.

  • other – second input array or container. Must be compatible with self (see broadcasting). Should have a numeric data type.

Returns:

ret – a container containing the element-wise sums. The returned array must have a data type determined by type-promotion.

Examples

>>> x = 1
>>> y = ivy.Container(a=3, b=4)
>>> z = x + y
>>> print(z)
{
    a: 4,
    b: 5
}
__rrshift__(other)[source]#

ivy.Container reverse special method for the right shift operator, calling operator.rshift for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input container. Should have an integer data type.

  • other – second input array or container. Must be compatible with self (see broadcasting). Should have an integer data type. Each element must be greater than or equal to 0.

Returns:

ret – a container containing the element-wise results. The returned array must have a data type determined by type-promotion.

Examples

>>> a = 64
>>> b = ivy.Container(a = ivy.array([0, 1, 2]),
...                   b = ivy.array([3, 4, 5]))
>>> y = a >> b
>>> print(y)
{
    a: ivy.array([64, 32, 16]),
    b: ivy.array([8, 4, 2])
}
__rshift__(other)[source]#

ivy.Container special method for the right shift operator, calling operator.rshift for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input container. Should have an integer data type.

  • other – second input array or container. Must be compatible with self (see broadcasting). Should have an integer data type. Each element must be greater than or equal to 0.

Returns:

ret – a container containing the element-wise results. The returned array must have a data type determined by type-promotion.

Examples

With Number instances at the leaves:

>>> x = ivy.Container(a=128, b=43)
>>> y = ivy.Container(a=5, b=3)
>>> z = x >> y
>>> print(z)
{
    a: 4,
    b: 5
}

With ivy.Array instances at the leaves:

>>> x = ivy.Container(a=ivy.array([16, 40, 120]),
...                   b=ivy.array([15, 45, 143]))
>>> y = ivy.Container(a=ivy.array([1, 2, 3]),
...                   b=ivy.array([0, 3, 4]))
>>> z = x >> y
>>> print(z)
{
    a: ivy.array([8, 10, 15]),
    b: ivy.array([15, 5, 8])
}

With a mix of ivy.Container and ivy.Array instances:

>>> x = ivy.Container(a=ivy.array([16, 40, 120]),
...                   b=ivy.array([15, 45, 143]))
>>> y = ivy.array([1, 2, 3])
>>> z = x >> y
>>> print(z)
{
    a: ivy.array([8, 10, 15]),
    b: ivy.array([7, 11, 17])
}
__rsub__(other)[source]#

ivy.Container reverse special method for the subtract operator, calling operator.sub for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input container. Should have a numeric data type.

  • other – second input array or container. Must be compatible with self (see broadcasting). Should have a numeric data type.

Returns:

ret – a container containing the element-wise differences. The returned array must have a data type determined by type-promotion.

Examples

>>> x = 1
>>> y = ivy.Container(a=3, b=4)
>>> z = x - y
>>> print(z)
{
    a: -2,
    b: -3
}
__sub__(other)[source]#

ivy.Container special method for the subtract operator, calling operator.sub for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input container. Should have a numeric data type.

  • other – second input array or container. Must be compatible with self (see broadcasting). Should have a numeric data type.

Returns:

ret – a container containing the element-wise differences. The returned array must have a data type determined by type-promotion.

Examples

With Number instances at the leaves:

>>> x = ivy.Container(a=1, b=2)
>>> y = ivy.Container(a=3, b=4)
>>> z = x - y
>>> print(z)
{
    a: -2,
    b: -2
}

With ivy.Array instances at the leaves:

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

With a mix of ivy.Container and ivy.Array instances:

>>> x = ivy.Container(a=ivy.array([[4.], [5.], [6.]]),
...                   b=ivy.array([[5.], [6.], [7.]]))
>>> y = ivy.array([[1.1, 2.3, -3.6]])
>>> z = x - y
>>> print(z)
{
    a: ivy.array([[2.9, 1.7, 7.6],
                  [3.9, 2.7, 8.6],
                  [4.9, 3.7, 9.6]]),
    b: ivy.array([[3.9, 2.7, 8.6],
                  [4.9, 3.7, 9.6],
                  [5.9, 4.7, 10.6]])
}
__truediv__(other)[source]#

ivy.Container special method for the divide operator, calling operator.truediv for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input container. Should have a numeric data type.

  • other – second input array or container. Must be compatible with self (see broadcasting). Should have a numeric data type.

Returns:

ret – a container containing the element-wise differences. The returned array must have a data type determined by type-promotion.

Examples

With Number instances at the leaves:

>>> x = ivy.Container(a=1, b=2)
>>> y = ivy.Container(a=5, b=4)
>>> z = x / y
>>> print(z)
{
    a: 0.2,
    b: 0.5
}

With ivy.Array instances at the leaves:

>>> x = ivy.Container(a=ivy.array([1, 2, 3]),
...                   b=ivy.array([4, 3, 2]))
>>> y = ivy.Container(a=ivy.array([4, 5, 6]),
...                   b=ivy.array([6, 5, 4]))
>>> z = x / y
>>> print(z)
{
    a: ivy.array([0.25, 0.40000001, 0.5]),
    b: ivy.array([0.66666669, 0.60000002, 0.5])
}
__xor__(other)[source]#

ivy.Container special method for the ge operator, calling operator.ge for each of the corresponding leaves of the two containers.

Parameters:

  • self – first input Container.

  • other – second input Container. Arrays inside must be compatible with x1 (see broadcasting). Should have an integer or boolean data type.

Returns:

ret – a container containing the element-wise results. Any returned arrays inside must have a data type determined by type-promotion.

Examples

With ivy.Container instances:

>>> x = ivy.Container(a=ivy.array([89]), b=ivy.array([2]))
>>> y = ivy.Container(a=ivy.array([12]), b=ivy.array([3]))
>>> z = x ^ y
>>> print(z)
{
    a: ivy.array([85]),
    b: ivy.array([1])
}

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

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