I am following the example code for Transfer Learning on Pytorch documentation.  My dataset though is not images and instead is numpy array with X size is 15 (read from 15 pixels) and y is uniques classes with 5 values (1,2,3,4,5). I explain my approach step by step:
import numpy as np
import time 
import copy
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, TensorDataset, DataLoader, random_split
import torch.nn.functional as F

Here is how my X[0] and y[0] look like:
(array([739, 742, 734, 732, 746, 734, 736, 737, 728, 742, 741, 736, 736, 741, 316]), array([2]))
I create my dataset as:
class MyDataset(Dataset):
    def __init__(self, data, target, transform=None):
        self.data = torch.from_numpy(data).float()
        self.target = torch.from_numpy(target).long()
        self.transform = transform
        
    def __getitem__(self, index):
        x = self.data[index]
        y = self.target[index]
        
        if self.transform:
            x = self.transform(x)
        
        return x, y
    
    def __len__(self):
        return len(self.data)

I split the X, y for training and validation as:
full_ds = MyDataset(X, y)

trn_size = int(0.8*len(full_ds))
val_size = len(full_ds) - trn_size
trn_ds, val_ds = random_split(full_ds, [trn_size, val_size])

datasets = {'train': trn_ds, 'val': val_ds}
dataloaders = {x: DataLoader(datasets[x], batch_size=4, shuffle=True) for x in ['train', 'val']}
dataset_sizes = {x: len(datasets[x]) for x in ['train', 'val']}

My model, optimizer and loss are:
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(15, 8)
        self.fc2 = nn.Linear(8, 5)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

net = Net()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
criterion = nn.NLLLoss()

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

and as i mentioned earlier my training function follows Pytorch standard example:
def train_model(model, criterion, optimizer, num_epochs=10):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model

When running training function, I am getting the following runtime error:
RuntimeError: multi-target not supported at /Users/administrator/nightlies/pytorch-1.0.0/wheel_build_dirs/conda_3.7/conda/conda-bld/pytorch_1544144746443/work/aten/src/THNN/generic/ClassNLLCriterion.c:21

I am stuck here and I would appreciate your help!