tensornetwork.CopyNode¶

class tensornetwork.CopyNode(rank: int, dimension: int, name: Optional[str] = None, axis_names: Optional[List[str]] = None, backend: Optional[str] = None, dtype: Type[numpy.number] = <class 'numpy.float64'>)¶

__init__(rank: int, dimension: int, name: Optional[str] = None, axis_names: Optional[List[str]] = None, backend: Optional[str] = None, dtype: Type[numpy.number] = <class 'numpy.float64'>) → None¶

Initialize a CopyNode:

Parameters:

rank – The rank of the tensor.
dimension – The dimension of each leg.
name – A name for the node.
axis_names – axis_names for the node.
backend – An optional backend for the node. If None, a default backend is used
dtype – The dtype used to initialize a numpy-copy node. Note that this dtype has to be a numpy dtype, and it has to be compatible with the dtype of the backend, e.g. for a tensorflow backend with a tf.Dtype=tf.floa32, dtype has to be np.float32.

Methods

`__init__`(rank, dimension, name, axis_names, …)	Initialize a CopyNode:
`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.
`compute_contracted_tensor`()	Compute tensor corresponding to contraction of self with neighbors.
`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_partners`()
`get_rank`()	Return rank of tensor represented by self.
`get_tensor`()
`has_dangling_edge`()
`has_nondangling_edge`()
`make_copy_tensor`(rank, dimension, dtype)
`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`

compute_contracted_tensor() → Any¶: Compute tensor corresponding to contraction of self with neighbors.

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

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.