I'm asking you a question about a tensor dimension error while using python's pytorch.

import torch

import torch.nn as nn

import torch.nn.functional as F

import torch.optim as optim

learning_rate=0.001

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

class Neural(nn.Module):
    def __init__(self):
        super(Neural, self).__init__()
        self.fc1=nn.Linear(1,10)
        self.fc2=nn.Linear(10,1)

        self.optimizer=optim.Adam(self.parameters(),lr=learning_rate)

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

        self.to(self.device)

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

    def train_net(self,X,Y):
        y_pred=self.forward(X)

        loss=MSE((y_pred-Y)**2)

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

model=Neural()

X=torch.tensor([1,2,3,4,5,6], dtype=torch.float).to(device)
Y=torch.tensor([3,6,9,12,15,18], dtype=torch.float).to(device)
print(X[1])
model.forward(X[1])