Address printing inconsistency between float and complex tensors (#35841)
Summary:
See issue [https://github.com/pytorch/pytorch/issues/33494 Complex number printing inconsistent with float](https://github.com/pytorch/pytorch/issues/33494).
Changes introduces an optional argument in Formatter's ```format``` function to discern whether a tensor is a float tensor or not. This way, there is consistency between float tensors and complex tensors so that the complex tensors print in the same manner as float tensors:
- Only a decimal point and no zeros for integer values.
- Trailing zeros only if the value is truly a float.
- White space introduced to fill the gap so that +/- symbols and commas align.
Here are some example outputs.
```
print(torch.zeros((2,2), dtype=torch.float64))
```
yields
```
tensor([[0., 0.],
[0., 0.]], dtype=torch.float64)
```
```
print(torch.zeros((2,2), dtype=torch.complex64))
```
previously yielded
```
tensor([[(0.0000 + 0.0000j), (0.0000 + 0.0000j)],
[(0.0000 + 0.0000j), (0.0000 + 0.0000j)]], dtype=torch.complex64)
```
and now yields
```
tensor([[(0 + 0.j), (0 + 0.j)],
[(0 + 0.j), (0 + 0.j)]], dtype=torch.complex64)
```
This new print version is more consistent with float tensor's pretty print.
The following example mixes integer and decimals:
```
print(torch.tensor([[1 + 1.340j, 3 + 4j], [1.2 + 1.340j, 6.5 + 7j]], dtype=torch.complex64))
```
This yields:
```
tensor([[ (1.0000 + 1.3400j),
(3.0000 + 4.0000j)],
[ (1.2000 + 1.3400j),
(6.5000 + 7.0000j)]], dtype=torch.complex64)
```
The following example
```
torch.tensor([1,2,3,4.5])
```
yields
```
tensor([1.0000, 2.0000, 3.0000, 4.5000]) .
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35841
Differential Revision: D20893848
Pulled By: anjali411
fbshipit-source-id: f84c533b8957a1563602439c07e60efbc79691bc
