mat1 and mat2 shapes cannot be multiplied (1×7 and 1×1)

I am using a linear regression model in PyTorch to predict number of cars sold from car price using fake data:

car_price_tensor
tensor([3., 4., 5., 6., 7., 8., 9.])

number_of_car_sell_tensor
tensor([[7.5000],
        [7.0000],
        [6.5000],
        [6.0000],
        [5.5000],
        [5.0000],
        [4.5000]])

Here’s the network:

import torch.nn as nn
from torch import optim

class LinearRegression(nn.Module):
    def __init__(self, in_dim, out_dim):
        super(LinearRegression, self).__init__()
        self.linear = nn.Linear(in_dim, out_dim, bias=True)
    
    def forward(self, x):
        return self.linear(x)
    
in_dim = 1
out_dim = 1
model = LinearRegression(in_dim,out_dim) 
loss_fn = nn.MSELoss()
lr = 1e-3
epochs = 40
optimizer = optim.SGD(model.parameters(), lr=lr)
X = car_price_tensor
y = number_of_car_sell_tensor


loss_list = []
for epoch in range(epochs):
    out = model(X)
    loss = loss_fn(out, y)
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()
    loss_list.append(loss/len(X))
    print("Epoch: {} train loss: {}".format(epoch+1, loss/len(X)))

I receive the following error: mat1 and mat2 shapes cannot be multiplied (1x7 and 1x1)

How can I get the network to work properly?