# TENSORINV

The TENSORINV node is based on a numpy or scipy function. The description of that function is as follows: Compute the 'inverse' of an N-dimensional array. The result is an inverse for 'a' relative to the tensordot operation "tensordot(a, b, ind)", i.e. up to floating-point accuracy, "tensordot(tensorinv(a), a, ind)" is the "identity" tensor for the tensordot operation. Params: a : array_like Tensor to 'invert'. Its shape must be 'square', i.e. "prod(a.shape[:ind]) == prod(a.shape[ind:])". ind : int Number of first indices that are involved in the inverse sum. Must be a positive integer, default is 2. Returns: out : DataContainer type 'ordered pair', 'scalar', or 'matrix'
Python Code
from flojoy import flojoy, Matrix, Scalar
import numpy as np

import numpy.linalg

@flojoy
def TENSORINV(
default: Matrix,
ind: int = 2,
) -> Matrix | Scalar:
"""The TENSORINV node is based on a numpy or scipy function.

The description of that function is as follows:

Compute the 'inverse' of an N-dimensional array.

The result is an inverse for 'a' relative to the tensordot operation "tensordot(a, b, ind)",
i.e. up to floating-point accuracy, "tensordot(tensorinv(a), a, ind)" is the "identity" tensor for the tensordot operation.

Parameters
----------
a : array_like
Tensor to 'invert'.
Its shape must be 'square', i.e. "prod(a.shape[:ind]) == prod(a.shape[ind:])".
ind : int, optional
Number of first indices that are involved in the inverse sum.
Must be a positive integer, default is 2.

Returns
-------
DataContainer
type 'ordered pair', 'scalar', or 'matrix'
"""

result = numpy.linalg.tensorinv(
a=default.m,
ind=ind,
)

if isinstance(result, np.ndarray):
result = Matrix(m=result)
else:
assert isinstance(
result, np.number | float | int
), f"Expected np.number, float or int for result, got {type(result)}"
result = Scalar(c=float(result))

return result


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