Computes the Pedersen commitment for a given input data using `grumpkin` curve elements.

In total, the function computes `data.len()` commitments,
which is related to the total number of columns in the data table. The commitment
results are stored as 512-bit `grumpkin` curve point in affine form in the `commitments` variable.

The `j`-th Pedersen commitment is a 512-bit `grumpkin` curve point `C_j` over the
`grumpin` elliptic curve that is cryptographically binded to a data message vector `M_j`. This `M_j` vector is populated according to the type of the `data` given.

For an input data table specified as a [crate::sequence::Sequence] slice view, we populate `M_j` as follows:

```text
let el_size = data[j].element_size; // sizeof of each element in the current j-th column
let num_rows = data[j].data_slice.len() / el_size; // number of rows in the j-th column

let M_j = [
   data[j].data_slice[0:el_size],
   data[j].data_slice[el_size:2*el_size],
   data[j].data_slice[2*el_size:3*el_size],
   .,
   .,
   .,
   data[j].data_slice[(num_rows-1)*el_size:num_rows*el_size]
];
```

This message `M_j` cannot be decrypted from `C_j`. The curve point `C_j`
is generated in a unique way using `M_j` and a
set of 768-bit `grumpkin` curve elements in projective form `G_i`, called row generators.
Although our GPU code uses 768-bit generators during the scalar 
multiplication, these generators are passed as 512-bit `grumpkin` curve elements in affine form
and only converted to 768-bit projective elements inside the GPU/CPU.

The total number of generators used to compute `C_j` is equal to 
the number of `num_rows` in the `data[j]` sequence. The following formula
is specified to obtain the `C_j` commitment when the input table is a 
[crate::sequence::Sequence] view:

```text
let C_j_temp = 0; // this is a 768-bit grumpkin curve element in projective form

for j in 0..num_rows {
    let G_i = generators[j].decompress(); // we decompress to convert 512-bit to 768-bit points
    let curr_data_ji = data[j].data_slice[i*el_size:(i + 1)*el_size];
    C_j_temp = C_j_temp + curr_data_ji * G_i;
}

let C_j = into_affine(C_j_temp); // this is a 512-bit grumpkin point
```

Ps: the above is only illustrative code. It will not compile.

Here `curr_data_ji` are simply 256-bit scalars, `C_j_temp` and `G_i` are
768-bit `grumpkin` curve elements in projective form and `C_j` is a 512-bit `grumpkin` point in affine form.

Given `M_j` and `G_i`, it is easy to verify that the Pedersen
commitment `C_j` is the correctly generated output. However,
the Pedersen commitment generated from `M_j` and `G_i` is cryptographically
binded to the message `M_j` because finding alternative inputs `M_j*` and 
`G_i*` for which the Pedersen commitment generates the same point `C_j`
requires an infeasible amount of computation.

To guarantee proper execution, so that the backend is correctly set,
this `compute_grumpkin_commitments_with_generators` always calls the `init_backend()` function.

Portions of this documentation were extracted from
[here](findora.org/faq/crypto/pedersen-commitment-with-elliptic-curves/)

# Arguments

* `commitments` - A sliced view of an un compressed `grumpkin` curve element memory area where the 
               512-bit point results will be written to. Please,
               you need to guarantee that this slice captures exactly
               `data.len()` element positions.

* `data` - A generic sliced view `T` of a [crate::sequence::Sequence], 
        which captures the slices of contiguous `u8` memory elements.
        You need to guarantee that the contiguous `u8` slice view
        captures the correct amount of bytes that can reflect
        your desired amount of `num_rows` in the sequence. After all,
        we infer the `num_rows` from `data[i].data_slice.len() / data[i].element_size`.

* `generators` - A sliced view of a `grumpkin` curve affine element memory area where the
              512-bit point generators used in the commitment computation are
              stored. Bear in mind that the size of this slice must always be greater
              or equal to the longest sequence, in terms of rows, in the table.

# Asserts

If the longest sequence in the input data is bigger than the generators length, or if
the `data.len()` value is different from the `commitments.len()` value.

# Panics

If the compute commitments execution in the GPU / CPU fails.