code: def create_wrapped_pytorch(width, dropout, nI, nO):
      import torch
      import torch.nn
      import torch.nn.functional as F
  
      class PyTorchModel(torch.nn.Module):
          def __init__(self, width, nO, nI, dropout):
              super(PyTorchModel, self).__init__()
              self.dropout1 = torch.nn.Dropout2d(dropout)
              self.dropout2 = torch.nn.Dropout2d(dropout)
              self.fc1 = torch.nn.Linear(nI, width)
              self.fc2 = torch.nn.Linear(width, nO)
  
          def forward(self, x):
              x = F.relu(x)
              x = self.dropout1(x)
              x = self.fc1(x)
              x = F.relu(x)
              x = self.dropout2(x)
              x = self.fc2(x)
              output = F.log_softmax(x, dim=1)
              return output
  
      return PyTorchWrapper(PyTorchModel(width, nO, nI, dropout))
  
file_path: thinc/thinc/tests/layers/test_mnist.py
imports: import pytest
  from thinc.api import Adam, PyTorchWrapper, Relu, Softmax, TensorFlowWrapper, chain, clone, get_current_ops
  from thinc.compat import has_tensorflow, has_torch
label_desc: Use image node_id 3 for calling a global function with example usage: create_wrapped_pytorch(width, dropout, nI, nO) and returns: PyTorchWrapper, output