to practice and get deeper understanding i tried to use a simple sequential model in tabular learner

for simplicity i used the iris dataset

```
from fastai.tabular.all import *
import torch.nn as nn
df = pd.read_csv('iris.csv')
split = TrainTestSplitter(random_state=42)(df)
df.species = pd.Categorical(df.species)
dls = TabularPandas(df, splits=split, procs=[Normalize], cat_names=[], cont_names=list(df.columns[:-1]), y_names='species', y_block=CategoryBlock()).dataloaders(bs=8)
class NNet(nn.Module):
def __init__(self):
super(NNet, self).__init__()
self.nnet = nn.Sequential(
nn.Linear(4,10),
nn.ReLU(),
nn.Linear(10,3),
nn.Softmax()
)
def forward(self, x, _):
return self.nnet(x.view(-1,4))
model = NNet()
learn = Learner(dls, model=model, metrics=accuracy, loss_func=CrossEntropyLossFlat)
learn.fit(10, lr=0.1)
```

when running learn.fit() i get this error:

**RuntimeError: Boolean value of Tensor with more than one value is ambiguous**

A. if i define learner the simplest way:

`learn = tabular_learner(dls, metrics=accuracy, layers=[10], cbs=ShowGraphCallback())`

everything works as expected, the training is done, i get predictions

what am i doing wrong?

B. also when checking dls.one_batch() i get:

(**tensor([], size=(8, 0), dtype=torch.int64),**

tensor([[-0.9863, -0.1293, -1.2199, -1.3095],

[ 1.0859, 0.5455, 1.1311, 1.2229],

…

what is this empty tensor on top, i can not figure it out?

thank you!