class_docstr: Basic Pytorch CNN for MNIST-like data.
class_docstr_tok: ["Basic","Pytorch","CNN","for","MNIST-like","data","."]
code: def forward(self, x, T=1.0):
      x = F.relu(F.max_pool2d(self.conv1(x), 2))
      x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
      x = x.view(-1, 320)
      x = F.relu(self.fc1(x))
      x = F.dropout(x, training=self.training)
      x = self.fc2(x)
      x = F.log_softmax(x, dim=1)
      return x
  
imports: from sklearn.base import BaseEstimator
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  from torch.autograd import Variable
  from torch.utils.data.sampler import SubsetRandomSampler
  import numpy
label_desc: Use image node_id 2 for calling the SimpleNet obj's underlying member method code with example usage: obj.forward(x, T) and returns: x