tensornetwork.Node¶

class tensornetwork.Node(tensor: Union[Any, tensornetwork.network_components.AbstractNode], name: Optional[str] = None, axis_names: Optional[List[str]] = None, backend: Union[str, tensornetwork.backends.abstract_backend.AbstractBackend, None] = None)¶

A Node represents a concrete tensor in a tensor network. The number of edges for a node represents the rank of that tensor.

For example:

A node with no edges means this node represents a scalar value.
A node with a single edge means this node is a vector.
A node with two edges represents a matrix.
A node with three edges is a tensor of rank 3, etc.

Each node can have an arbitrary rank/number of edges, each of which can have an arbitrary dimension.

__init__(tensor: Union[Any, tensornetwork.network_components.AbstractNode], name: Optional[str] = None, axis_names: Optional[List[str]] = None, backend: Union[str, tensornetwork.backends.abstract_backend.AbstractBackend, None] = None) → None¶

Create a node.

Parameters:

tensor – The concrete that is represented by this node, or a AbstractNode object. If a tensor is passed, it can be be either a numpy array or the tensor-type of the used backend. If a AbstractNode is passed, the passed node has to have the same backend as given by backend.
name – Name of the node. Used primarily for debugging.
axis_names – List of names for each of the tensor’s axes.
backend – The name of the backend or an instance of a AbstractBackend.

Raises:

ValueError – If there is a repeated name in axis_names or if the length doesn’t match the shape of the tensor.

Methods

`__init__`(tensor, …)	Create a node.
`add_axis_names`(axis_names)	Add axis names to a Node.
`add_edge`(edge, axis, str], override)	Add an edge to the node on the given axis.
`copy`(conjugate)
`disable`()
`fresh_edges`(axis_names)
`get_all_dangling`()	Return the set of dangling edges connected to this node.
`get_all_edges`()
`get_all_nondangling`()	Return the set of nondangling edges connected to this node.
`get_axis_number`(axis, int])	Get the axis number for a given axis name or value.
`get_dimension`(axis, int])	Get the dimension of the given axis.
`get_edge`(axis, str])
`get_rank`()	Return rank of tensor represented by self.
`get_tensor`()
`has_dangling_edge`()
`has_nondangling_edge`()
`op_protection`(other, float, complex, Node])
`reorder_axes`(perm)	Reorder axes of the node’s tensor.
`reorder_edges`(edge_order)	Reorder the edges for this given Node.
`set_name`(name)
`set_tensor`(tensor)
`tensor_from_edge_order`(perm)

Attributes

`axis_names`
`dtype`
`edges`
`name`
`shape`
`sparse_shape`
`tensor`

classmethod from_serial_dict(serial_dict) → tensornetwork.network_components.Node¶

Return a node given a serialized dict representing it.

Parameters:	serial_dict – A python dict representing a serialized node.
Returns:	A node.

to_serial_dict() → Dict[KT, VT]¶

Return a serializable dict representing the node.

Returns: A dict object.