Simple Inference

题面

Run inference on a PyTorch model. Given an input tensor and a trained torch.nn.Linear model, compute the forward pass and store the result in the output tensor.

The model performs a linear transformation: output = input @ weight.T + bias where weight has shape [output_size, input_size] and bias has shape [output_size].

Implementation Requirements

• Use PyTorch's built-in functions and operations
• The solve function signature must remain unchanged
• The final result must be stored in the output tensor
• The model is already loaded and ready for inference

Examples

Example 1:

Input:  input = [[1.0, 2.0]]  (batch_size=1, input_size=2)
        model: Linear layer with weight=[[0.5, 1.0], [1.5, 0.5]], bias=[0.1, 0.2]
Output: output = [[2.6, 2.7]]  (batch_size=1, output_size=2)

Example 2:

Input:  input = [[1.0], [2.0], [3.0]]  (batch_size=3, input_size=1)
        model: Linear layer with weight=[[2.0]], bias=[1.0]
Output: output = [[3.0], [5.0], [7.0]]  (batch_size=3, output_size=1)

Constraints

• 1 ≤ batch_size ≤ 1,000
• 1 ≤ input_size ≤ 1,000
• 1 ≤ output_size ≤ 1,000
• -10.0 ≤ input values ≤ 10.0
• Performance is measured with batch_size = 1,000