code: def __init__(self, input_size: int, hidden_size: int):
      torch.manual_seed(7)
      super(FeedForward, self).__init__()
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.fc1 = nn.Linear(self.input_size, self.hidden_size)
      self.relu1 = nn.ReLU()
      self.fc2 = nn.Linear(self.hidden_size, self.hidden_size)
      self.relu2 = nn.ReLU()
      self.fc3 = nn.Linear(self.hidden_size, 1)
      self.sigmoid = nn.Sigmoid()
      self.identity = nn.Identity()
  
imports: import logging
  import os
  from typing import Any, List, Tuple, Union
  import numpy
  import pytest
  from sklearn.datasets import make_blobs
  import torch
  from torch.cuda.amp.autocast_mode import autocast
  import torch.nn
  from torch.utils.data import DataLoader
  import torchvision
  import torchvision.transforms
  from fairscale.fair_dev.common_paths import DATASET_CACHE_ROOT
  from fairscale.fair_dev.testing.testing import skip_a_test_if_in_CI
  from fairscale.optim.layerwise_gradient_scaler import LayerwiseGradientScaler
label_desc: Use image node_id 1 to create a new FeedForward object from inherited base classes: torch.nn with example: obj = FeedForward(input_size, hidden_size)