Fit unequal data into Linear Regression Model

Question

How do I fit two unproportional arrays to a regression model? Is it possible to resize/reshape one without loosing the data?

I used the code from here but my train data has a completely different shape. It's 10000x1 and 10000x50.

There is an example below:

x_values
# Convert to numpy
x_train = np.array(x_values, dtype=np.float32)
x_train.shape
# IMPORTANT: 2D required
x_train = x_train.reshape(-1, 2)
x_train.shape

#(10, 2)

y_values
y_train = np.array(y_values, dtype=np.float32)
y_train.shape
# IMPORTANT: 2D required
y_train = y_train.reshape(-1, 1)
y_train.shape

#(10, 1)

With the following code I get RuntimeError: mat1 and mat2 shapes cannot be multiplied (10x2 and 1x1)

import torch
import torch.nn as nn
# Create class
class LinearRegressionModel(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LinearRegressionModel, self).__init__()
        self.linear = nn.Linear(input_dim, output_dim)  
    
    def forward(self, x):
        out = self.linear(x)
        return out
    
input_dim = 1
output_dim = 1

model = LinearRegressionModel(input_dim, output_dim)
criterion = nn.MSELoss()
learning_rate = 0.01

optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

epochs= 10
for epoch in range(epochs):
    epoch += 1
    # Convert numpy array to torch Variable
    inputs = torch.from_numpy(x_train).requires_grad_()
    labels = torch.from_numpy(y_train)

    # Clear gradients w.r.t. parameters
    optimizer.zero_grad() 
    
    # Forward to get output
    outputs = model(inputs)
    
    # Calculate Loss
    loss = criterion(outputs, labels)
    
    # Getting gradients w.r.t. parameters
    loss.backward()
    
    # Updating parameters
    optimizer.step()
    
    print('epoch {}, loss {}'.format(epoch, loss.item()))