code: def forward(self, x):
      x = torch.unsqueeze(x, dim=1)  # (1, 5) -> (1, 1, 5)
      x = self.linear(x)  # (1, 1, 3)
      x = torch.squeeze(x)  # (3,)
      x = torch.relu(x)  # (3,)
      return x
  
imports: import math
  from pathlib import Path
  import numpy
  import pytest
  import torch
  import torch.nn
  from torch.nn import AvgPool1d, AvgPool2d, MaxPool1d, MaxPool2d
  from hls4ml.converters import convert_from_pytorch_model
  from hls4ml.utils.config import config_from_pytorch_model
label_desc: Use image node_id 2 for calling the SqueezeModel obj's underlying member method code with example usage: obj.forward(x) and returns: x