We import PyTorch.

```
import torch
```

Then we print the torch version we are using.

```
print(torch.__version__)
```

We’re using 0.2.0_4.

We construct an uninitialized PyTorch tensor, we define a variable x and set it equal to torch.Tensor(3, 3, 3).

```
x = torch.Tensor(3, 3, 3)
```

We can then print that tensor to see what we created.

```
print(x)
```

A few things to note looking at the printing:

First - All the entries are uninitialized.

Second - The last line tells us that it is a FloatTensor.

Third - Printing the Tensor tells us what type of PyTorch Tensor it is.

And Four - By default, PyTorch Tensors are created using floating numbers.

Next let's create a second tensor, random_tensor, using the PyTorch rand functionality.

```
random_tensor = torch.rand(3, 3, 3)
```

This random_tensor tensor is a PyTorch Tensor where each entry is a random number pulled from a uniform distribution from 0 to 1.

To see what the random_tensor Type is, without actually printing the whole Tensor, we can pass the random_tensor to the Python type function.

```
type(random_tensor)
```

From this you can see that it is a PyTorch FloatTensor.

Finally, we define an uninitialized PyTorch IntTensor which only holds integers.

```
integers_only = torch.IntTensor(2, 2, 2)
```

Even though we know it's an IntTensor since we defined it that way, we can still check the type of the tensor.

```
type(integers_only)
```

We can see that it is in fact a PyTorch IntTensor.