1. Explicit value means the non-zero value in a sparse tensor. If
explicitVal is set, then all the non-zero values in the tensor have the
same explicit value. The default value Attribute() indicates that it is
not set.

2. Implicit value means the "zero" value in a sparse tensor. If
implicitVal is set, then the "zero" value in the tensor is equal to the
implicit value. For now, we only support `0` as the implicit value but
it could be extended in the future. The default value Attribute()
indicates that the implicit value is `0` (same type as the tensor
element type).

Example:

```
#CSR = #sparse_tensor.encoding<{
  map = (d0, d1) -> (d0 : dense, d1 : compressed),
  posWidth = 64,
  crdWidth = 64,
  explicitVal = 1 : i64,
  implicitVal = 0 : i64
}>
```

Note: this PR tests that implicitVal could be set to other values as
well. The following PR will add verifier and reject any value that's not
zero for implicitVal.

Reverts llvm/llvm-project#81799 ; this broke the mlir gcc7 bot.

…ormat and properties instead.

…ct LevelType from LevelFormat and properties instead.

**Rationale**
We used to explicitly declare every possible combination between
`LevelFormat` and `LevelProperties`, and it now becomes difficult to
scale as more properties/level formats are going to be introduced.

1. Add python test for n out of m
2. Add more methods for python binding
3. Add verification for n:m and invalid encoding tests
4. Add e2e test for n:m

Previous PRs for n:m #80501 #79935

1. Add parsing methods for block[n, m].
2. Encode n and m with the newly extended 64-bit LevelType enum.
3. Update 2:4 methods names/comments to n:m.

The "Dim" prefix is a legacy left-over that no longer makes sense, since
we have a very strict "Dimension" vs. "Level" definition for sparse
tensor types and their storage.