How to use the fastai.vision.models function in fastai

def test_model_to_learner():
    # Test if the function loads an ImageNet model (ResNet) trainer
    learn = model_to_learner(models.resnet34(pretrained=True))
    assert len(learn.data.classes) == 1000  # Check Image net classes
    assert isinstance(learn.model, models.ResNet)

    # Test with SqueezeNet
    learn = model_to_learner(models.squeezenet1_0())
    assert len(learn.data.classes) == 1000
    assert isinstance(learn.model, models.SqueezeNet)

# Split squeezenet model on maxpool layers
def _squeezenet_split(m:nn.Module): return (m[0][0][5], m[0][0][8], m[1])
def _densenet_split(m:nn.Module): return (m[0][0][7],m[1])
def _vgg_split(m:nn.Module): return (m[0][0][22],m[1])
def _alexnet_split(m:nn.Module): return (m[0][0][6],m[1])

_default_meta    = {'cut':None, 'split':_default_split}
_resnet_meta     = {'cut':-2, 'split':_resnet_split }
_squeezenet_meta = {'cut':-1, 'split': _squeezenet_split}
_densenet_meta   = {'cut':-1, 'split':_densenet_split}
_vgg_meta        = {'cut':-1, 'split':_vgg_split}
_alexnet_meta    = {'cut':-1, 'split':_alexnet_split}

model_meta = {
    models.resnet18 :{**_resnet_meta}, models.resnet34: {**_resnet_meta},
    models.resnet50 :{**_resnet_meta}, models.resnet101:{**_resnet_meta},
    models.resnet152:{**_resnet_meta},

    models.squeezenet1_0:{**_squeezenet_meta},
    models.squeezenet1_1:{**_squeezenet_meta},

    models.densenet121:{**_densenet_meta}, models.densenet169:{**_densenet_meta},
    models.densenet201:{**_densenet_meta}, models.densenet161:{**_densenet_meta},
    models.vgg11_bn:{**_vgg_meta}, models.vgg13_bn:{**_vgg_meta}, models.vgg16_bn:{**_vgg_meta}, models.vgg19_bn:{**_vgg_meta},
    models.alexnet:{**_alexnet_meta}}

def cnn_config(arch):
    "Get the metadata associated with `arch`."
    torch.backends.cudnn.benchmark = True
    return model_meta.get(arch, _default_meta)

def has_pool_type(m):

# Set parameters based on your selected model.

# In[6]:


if MODEL_TYPE == "high_accuracy":
    ARCHITECTURE = models.resnet50
    IM_SIZE = 500

if MODEL_TYPE == "fast_inference":
    ARCHITECTURE = models.resnet18
    IM_SIZE = 300

if MODEL_TYPE == "small_size":
    ARCHITECTURE = models.squeezenet1_1
    IM_SIZE = 300


# We'll automatically determine if your dataset is a multi-label or traditional (single-label) classification problem. To do so, we'll use the `is_data_multilabel` helper function. In order to detect whether or not a dataset is multi-label, the helper function will check to see if the datapath contains a csv file that has a column 'labels' where the values are space-delimited. You can inspect the function by calling `is_data_multilabel??`.
#
# This function assumes that your multi-label dataset is structured in the recommended format shown in the [multilabel notebook](02_multilabel_classification.ipynb).

# In[7]:


multilabel = is_data_multilabel(DATA_PATH)
metric = accuracy if not multilabel else hamming_accuracy


# ## Pre-processing <a name="preprocessing"></a>
#

# Make sure that `MODEL_TYPE` is correctly set.

# In[5]:


assert MODEL_TYPE in ["high_accuracy", "fast_inference", "small_size"]


# Set parameters based on your selected model.

# In[6]:


if MODEL_TYPE == "high_accuracy":
    ARCHITECTURE = models.resnet50
    IM_SIZE = 500

if MODEL_TYPE == "fast_inference":
    ARCHITECTURE = models.resnet18
    IM_SIZE = 300

if MODEL_TYPE == "small_size":
    ARCHITECTURE = models.squeezenet1_1
    IM_SIZE = 300


# We'll automatically determine if your dataset is a multi-label or traditional (single-label) classification problem. To do so, we'll use the `is_data_multilabel` helper function. In order to detect whether or not a dataset is multi-label, the helper function will check to see if the datapath contains a csv file that has a column 'labels' where the values are space-delimited. You can inspect the function by calling `is_data_multilabel??`.
#
# This function assumes that your multi-label dataset is structured in the recommended format shown in the [multilabel notebook](02_multilabel_classification.ipynb).

# In[7]: