```
import pennylane as qml
from pennylane import numpy as np
n_qubits = 2
dev = qml.device("default.qubit", wires=n_qubits)
weights = np.random.random(size=(n_qubits, 2), requires_grad=True)
x = np.random.random(size=(2,))
@qml.qnode(dev)
def circuit(weights, x):
for i in range(n_qubits):
qml.RX(x[i], wires=i)
qml.RY(weights[i, 0], wires=i)
qml.RZ(weights[i, 1], wires=i)
return qml.expval(qml.PauliZ(0))
def compute_qfim_and_ggrad(weights, x):
cost_fn = lambda w: circuit(w, x)
qfim = qml.gradients.quantum_fisher(circuit)(weights, x)
ggrad = qml.grad(cost_fn)(weights)
return qfim, ggrad
qfim, ggrad = compute_qfim_and_ggrad(weights, x)
print(qfim)
print("\nGradient (ggrad):")
print(ggrad)
metric_fn = qml.metric_tensor(circuit, approx="diag")(weights, x)
print(metric_fn)
```

It works, but how can i have diag elements or block-diagonal elements of qfim. Second question is qfim=4. metric tensor (qml.gradients.quantum_fisher — PennyLane 0.38.1 documentation). But on verification, it did not 4 times of metric tensor elements. is there anything mistake. Can you please help.

```
Name: PennyLane
Version: 0.38.0
Summary: PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
Home-page: https://github.com/PennyLaneAI/pennylane
Author:
Author-email:
License: Apache License 2.0
Location: /home/bhatia87/.conda/envs/cent7/2020.11-py38/xyz2/lib/python3.10/site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, packaging, pennylane-lightning, requests, rustworkx, scipy, toml, typing-extensions
Required-by: PennyLane_Lightning
Platform info: Linux-3.10.0-1160.108.1.el7.x86_64-x86_64-with-glibc2.17
Python version: 3.10.14
Numpy version: 1.23.5
Scipy version: 1.10.0
Installed devices:
- lightning.qubit (PennyLane_Lightning-0.38.0)
- default.clifford (PennyLane-0.38.0)
- default.gaussian (PennyLane-0.38.0)
- default.mixed (PennyLane-0.38.0)
- default.qubit (PennyLane-0.38.0)
- default.qubit.autograd (PennyLane-0.38.0)
- default.qubit.jax (PennyLane-0.38.0)
- default.qubit.legacy (PennyLane-0.38.0)
- default.qubit.tf (PennyLane-0.38.0)
- default.qubit.torch (PennyLane-0.38.0)
- default.qutrit (PennyLane-0.38.0)
- default.qutrit.mixed (PennyLane-0.38.0)
- default.tensor (PennyLane-0.38.0)
- null.qubit (PennyLane-0.38.0)
```