Tensor(1.0).item() vs float(Tensor(1.0))
If x is a torch.Tensor of dtype torch.float then are the operations x.item() and float(x) exactly the same?

The operations x.item() and float(x) are not the same.
From the documentation of item(), it can be used to get the value of tensor as a Python number(only from tensors containing a single value). It basically returns the value of the tensor as it is. It does not make any modifications to the tensor.
Where as float() is for converting its input to a floating point number, when possible. Find the documentation here.
To see the difference, consider another Tensor y of dtype int64:
import torch

y = torch.tensor(2)
print(y, y.dtype)
&gt;&gt;&gt; tensor(2) torch.int64

print('y.item(): {}, float(y): {}'.format(y.item(), float(y)))
&gt;&gt;&gt; y.item(): 2, float(y): 2.0

print(type(y.item()), type(float(y)))
&gt;&gt;&gt; &lt;class 'int'&gt; &lt;class 'float'&gt;

Note that float(y) does not convert the type in-place. You would need to assign it in case you need that change. Like:
z = float(y)
print('y.dtype: {}, type(z): {}'.format(y.dtype, type(z)))
&gt;&gt;&gt; y.dtype: torch.int64, type(z): &lt;class 'float'&gt;

We can see that z is not a torch.Tensor. It is simply a floating point number.
The float() operation is not to be confused with self.float(). This operation performs the Tensor dtype conversion (not in-place, needs assignment).
print('y.float(): {},\n y.float().dtype: {},\n y: {},\n y.dtype'.format(y.float(), y.float().dtype, y, y.dtype))

y.float(): 2.0,
y.float().dtype: torch.float32,
y: 2,
y.dtype: torch.int64

