# Numpy linalg tensorsolve() Function in Python with Example Numpy linalg tensorsolve() function is used to calculate the equation of ax=b for x. It is assumed that all x indices are summarized above the product and the right indices of a, as is done.

Numpy linalg tensorsolve() function is used to calculate the equation of ax=b for x. It is assumed that all x indices are summarized above the product and the right indices of a, as is done. For example, tensordot (a, x, axes = b.ndim).

#### Syntax

``numpy.linalg.tensorsolve(A, B, axes=None )``

#### Parameters

1. A: Coefficient tensor, condition b. status + Q. Q, Tuple, is equal to the shape of that sub-tensor with the correct number of its right indices and should be the same (pr) (Q) == prod (b) .shape) (when it is called a ‘square’).
2. B: Right-hand tensor, which can be of any shape.

#### Return Value

The linalg tensorsolve() function returns a ndarray of shape the same as Q.

The linalg tensorsolve() function throws LinAlgError if A is singular or not a square matrix.

#### Programming example

##### Program to show the working of tensorsolve():
``````## Program to show working of solve()
import numpy as np

## creating the array "a"
A = np.array([[3, 4, 5], [1, 2, 3], [2, 4, 5]])
B = np.array([9, 8, 7])
print("Array A is: \n", A)
print("Array B is : \n", B)

## Calculating the equation
ans = np.linalg.tensorsolve(A, B)

print("Answer of the equation is  :\n", ans)

## Checking if the answer if correct
print(np.allclose(np.dot(A, ans), B))``````

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