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

__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 – the dictionary the container should wrap around. Default is None.

  • queues – 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 – Size of leading dimension of the containers returned by each queue. Default is None.

  • container_combine_method – The method to use for combining containers arriving from different queues. Default is ivy.Container.cont_list_join

  • queue_timeout – The timeout when waiting for containers to arrive from the queues. Default is global.

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

  • key_length_limit – The maximum key length when printing the container. Default is None.

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

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

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

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

  • keyword_color_dict – A dict mapping keywords to their termcolor color codes for printing the container. (Default value = None)

  • rebuild_child_containers – 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.

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

  • alphabetical_keys – 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 in the container channel or in the container forum!