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).

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

**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’).**B:**Right-hand tensor, which can be of any shape.

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.

```
## 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)
## Printing the answer
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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