# Imports
import torch
from torch.utils.data import Dataset, DataLoader
print(torch.__version__)

# NN APIs
import torch.autograd                   # Computation Graph
from torch.autograd import Variable     # Variable node in computation graph
import torch.nn as nn                   # Neural Networks
import torch.nn.functional as F         # Layers, activations, losses and more
import torch.optim as optim             # optimizers: Adam, ...

# Distributed Training
import torch.distributed as dist        # distributed communication
from multiprocessing import Process     # memory sharing processes

# Torchvision
from torchvision import datasets, models, transforms  # vision datasets, vision models, image transforms
import torchvision.transforms as transforms           # composable transforms

# Torchscript and JIT
from torch.jit import script, trace     # hybrid frontend decorator and tracing jit
torch.jit.trace(func, example_inputs)
# takes your module or function and an example data input, and traces the computational steps 
# that the data encounters as it progresses through the model

@script  # decorator used to indicate data-dependent control flow within the code being traced