code: def __init__(self, width, oscillate):
      super(_NeuralF, self).__init__()
  
      # Use the same set of random weights for every instance.
      with random_seed_torch(0):
          self.linears = torch.nn.Sequential(
              torch.nn.Linear(2, width),
              torch.nn.Tanh(),
              torch.nn.Linear(width, 2),
              torch.nn.Tanh(),
          )
      self.nfe = 0
      self.oscillate = oscillate
  
file_path: torchdiffeq/tests/norm_tests.py
imports: import contextlib
  import unittest
  import torch
  import torchdiffeq
  from problems import DTYPES, DEVICES, ADAPTIVE_METHODS
label_desc: Use image node_id 1 to create a new _NeuralF object from inherited base classes: torch.nn with example: obj = _NeuralF(width, oscillate)