input_grads.py

import torch
from torch import nn

torch.manual_seed(0)
# We have to define ABC as a Parameter/Variable using requires_grad=True
# This makes ABC behave like a parameter of the model, so that it becomes a node in
# the computation graph
ABC = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)

F = nn.Sequential(
    nn.Linear(3, 8),
    nn.Sigmoid(),
    nn.Linear(8, 1),
)

y = F(ABC)
# backprogation requires grad_outputs
# it's usually the d Loss / dy,
# but in our case we have no loss function: Loss = y, and d Loss / dy = [1,...]
dy_dx = torch.autograd.grad(
    outputs=y, inputs=ABC, grad_outputs=torch.ones_like(y)
)

print("ABC:", ABC)
print("y:", y)
print("dy/dx:", dy_dx)