class_docstr: Test optimizers and related classes.
class_docstr_tok: ["Test","optimizers","and","related","classes","."]
docstr: test creating a KFAC optimizer
docstr_tok: ["test","creating","a","KFAC","optimizer"]
code: def test_KFAC(self):
      """test creating a KFAC optimizer"""
      import numpy as np
  
      np.random.seed(123)
      # Conv2d and Linear layers test(CNN classification)
      n_samples = 10
      n_features = 10
      n_tasks = 1
  
      X = np.random.rand(n_samples, 1, n_features, n_features)
      y = np.random.randint(2, size=(n_samples, n_tasks)).astype(
          np.float32
      )
      dataset = dc.data.NumpyDataset(X, y)
  
      metric = dc.metrics.Metric(dc.metrics.roc_auc_score)
      model = torch.nn.Sequential(
          torch.nn.Conv2d(1, 32, kernel_size=3, padding=1),
          torch.nn.Conv2d(32, 64, kernel_size=3, padding=1),
          torch.nn.Flatten(),
          torch.nn.Linear(64 * n_features * n_features, 20),
          torch.nn.ReLU(),
          torch.nn.Linear(20, n_tasks),
      )
      model = dc.models.TorchModel(
          model,
          dc.models.losses.L2Loss(),
          optimizers=optimizers.KFAC(
              model=model, learning_rate=0.003, Tinv=10
          ),
      )
      # Fit trained model
      model.fit(
          dataset,
          nb_epoch=100,
      )
  
      # Eval model on train
      scores = model.evaluate(dataset, [metric])
      assert scores[metric.name] > 0.9
  
imports: import deepchem
  import deepchem.models.optimizers
  import unittest
  import pytest
label_desc: Use image node_id 28 for calling the TestOptimizers obj's underlying member method code with example usage: obj.test_KFAC() without return types