class_docstr: test for:
      add, sub, mul, div, exp, matmul,
      relu, gelu, tanh, silu, sigmod, softmax, leaky_relu,
      size, unsqueeze, flatten, cat, slice, reshape, transpose, t, select, permute, constant_pad_nd, split
      mean, avg_pool2d, max_pool2d, sum, adaptive_avg_pool2d,
      to, Int, view,
      type_as, expand_as, contiguous,
  
  notes:
      'floor_divide' have no backward, then not be tested
class_docstr_tok: ["test","for",":","add",",","sub",",","mul",",","div",",","exp",",","matmul",",","relu",",","gelu",",","tanh",",","silu",",","sigmod",",","softmax",",","leaky_relu",",","size",",","unsqueeze",",","flatten",",","cat",",","slice",",","reshape",",","transpose",",","t",",","select",",","permute",",","constant_pad_nd",",","split","mean",",","avg_pool2d",",","max_pool2d",",","sum",",","adaptive_avg_pool2d",",","to",",","Int",",","view",",","type_as",",","expand_as",",","contiguous",",","notes",":","'floor_divide","'","have","no","backward",",","then","not","be","tested"]
code: def __init__(self):
      super().__init__()
      self.conv1 = torch.nn.Conv2d(1, 6, 5, 1)
      self.conv2 = torch.nn.Conv2d(6, 16, 5, 1)
      self.fccond = torch.nn.Linear(16 * 4 * 4, 16 * 4 * 4)
      self.fc1 = torch.nn.Linear(16 * 4 * 4, 120)
      self.fc2 = torch.nn.Linear(120, 84)
      self.fc3 = torch.nn.Linear(84, 10)
      self.pool1 = torch.nn.MaxPool2d((2, 2))
      self.pool2 = torch.nn.MaxPool2d((2, 2))
      self.pool3 = torch.nn.MaxPool2d((2, 2))
      self.pool4 = torch.nn.MaxPool2d((2, 2))
      self.pool5 = torch.nn.MaxPool2d((2, 2))
      self.logsoftmax = torch.nn.LogSoftmax()
      self.cond = torch.jit.script(CondModel())
  
imports: import unittest
  import torch
  import torch.nn.functional
  from nni.compression.pruning import L1NormPruner
  from nni.compression.speedup import ModelSpeedup
  from nni.compression.utils import compute_sparsity_compact2origin, compute_sparsity_mask2compact
label_desc: Use image node_id 1 to create a new TorchModel1 object from inherited base classes: torch.nn with example: obj = TorchModel1()