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$ ti test_verbose
[T 02/07/20 06:58:09.802] [logging.cpp:Logger@68] Taichi core started. Thread ID = 14291
[Taichi version 0.4.4, cpu only, commit c042fba7]

 *******************************************
 **     Taichi Programming Language       **
 *******************************************


Running python tests...

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import os
import numpy as np
import tvm
from tvm import relay
import torch
import torch.nn as nn
import torch.nn.functional as F

from torch.quantization import QuantStub, DeQuantStub
from torch.quantization import default_qconfig

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import numpy as np
import torch
import tvm
from tvm import relay
from torchvision import models

from torch_frontend import parse_script_module


class WrapperModule(torch.nn.Module):

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from collections import OrderedDict
from itertools import product
import torch
import numpy as np
from torchvision import models
import unittest
import random


def set_rng_seed(seed):

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In [9]: pt_result[0][:10]                                                                                                                                                                     
Out[9]: 
array([-1.8651257,  0.6217086,  1.0102764,  0.8548493,  0.8548493,
       -0.6217086,  0.6994221,  0.3108543, -0.9325628, -0.4662814],
      dtype=float32)

In [10]: tvm_result[0][:10]                                                                                                                                                                   
Out[10]: 
array([-1.8651257,  0.6217086,  1.0102764,  0.9325628,  0.8548493,
       -0.6994221,  0.6217086,  0.3108543, -0.9325628, -0.543995 ],

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WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.

v0.0.4
type tensor_int16_t {
  tensor_nil_int16,

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v0.0.4
def @main(%X: Tensor[(10, 10, 4), float32], %v26: Tensor[(4, 4), float32], %v25: Tensor[(4), float32]) -> Tensor[(10, 4), float32] {
  %0 = full(0 /* ty=int32 */, shape=[10, 4], dtype="float32") /* ty=Tensor[(10, 4), float32] */;
  %1 = full(0 /* ty=int32 */, shape=[10, 4], dtype="float32") /* ty=Tensor[(10, 4), float32] */;
  %14 = (
    let %while_loop: fn (int32, Tensor[(10, 4), float32], Tensor[(10, 4), float32]) -> (int32, Tensor[(10, 4), float32], Tensor[(10, 4), float32]) = fn (%i.1: int32, %y.5: Tensor[(10, 4), float32], %h.5: Tensor[(10, 4), float32]) -> (int32, Tensor[(10, 4), float32], Tensor[(10, 4), float32]) {
      %2 = less(%i.1, 10 /* ty=int32 */) /* ty=bool */;
      if (%2) {
        %3 = add(%i.1, 1 /* ty=int32 */) /* ty=int32 */;
        %4 = take(%X, %i.1, axis=0) /* ty=Tensor[(10, 4), float32] */;

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v0.0.4
type u {
  c0((int32, u[])),
  c1(()),
}

def @main(%q: u[]) -> int32 {
  @fn(%q) /* ty=int32 */
}

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graph(%self : __torch__.RNNLoop,
      %xs.1 : Tensor):
  %2 : bool = prim::Constant[value=1]() # dynamic_test.py:133:8
  %3 : None = prim::Constant()
  %4 : int = prim::Constant[value=10]() # dynamic_test.py:132:27
  %5 : int = prim::Constant[value=4]() # dynamic_test.py:132:31
  %6 : int = prim::Constant[value=0]() # dynamic_test.py:133:31
  %7 : int[] = prim::ListConstruct(%4, %5)
  %h.1 : Tensor = aten::zeros(%7, %3, %3, %3, %3) # dynamic_test.py:132:15
  %9 : int[] = prim::ListConstruct(%4, %5)

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# Licensed to the Apache Software Foundation (ASF) under one
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# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
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