Slice access analysis to eliminate slices and concats inside loops

### case I

```mlir
%235 = stablehlo.slice %iterArg_101 [0:1, 1:2033, 0:4080] {mhlo.sharding = "{devices=[1,4,8]<=[8,4]T(1,0)}"} : (tensor<1x2034x4080xf64>) -> tensor<1x2032x4080xf64> loc(#loc1095) // <-- this is just %240

....

%244 = stablehlo.concatenate %242, %240, %243, dim = 1 {mhlo.sharding = "{devices=[1,4,8]<=[8,4]T(1,0)}"} : (tensor<1x1x4080xf64>, tensor<1x2032x4080xf64>, tensor<1x1x4080xf64>) -> tensor<1x2034x4080xf64> loc(#loc1167)

return %iterArg_101 = %244
```

### case II

```mlir
%209 = stablehlo.slice %iterArg_102 [1:129, 1:2033, 0:4080] {mhlo.sharding = "{devices=[1,4,8]<=[8,4]T(1,0)}"} : (tensor<130x2034x4080xf64>) -> tensor<128x2032x4080xf64> loc(#loc1093) // same as slicing %254 along dim 0

%571 = stablehlo.slice %iterArg_102 [0:1, 0:1, 0:4080] {mhlo.sharding = "{devices=[1,4,8]<=[8,4]T(1,0)}"} : (tensor<130x2034x4080xf64>) -> tensor<1x1x4080xf64> loc(#loc) // <-- not needed

%572 = stablehlo.slice %iterArg_102 [129:130, 0:1, 0:4080] {mhlo.sharding = "{devices=[1,4,8]<=[8,4]T(1,0)}"} : (tensor<130x2034x4080xf64>) -> tensor<1x1x4080xf64> loc(#loc) // <-- not needed

%574 = stablehlo.slice %iterArg_102 [0:1, 2033:2034, 0:4080] {mhlo.sharding = "{devices=[1,4,8]<=[8,4]T(1,0)}"} : (tensor<130x2034x4080xf64>) -> tensor<1x1x4080xf64> loc(#loc) // <-- not needed

%575 = stablehlo.slice %iterArg_102 [129:130, 2033:2034, 0:4080] {mhlo.sharding = "{devices=[1,4,8]<=[8,4]T(1,0)}"} : (tensor<130x2034x4080xf64>) -> tensor<1x1x4080xf64> loc(#loc) // <-- not needed

%254 = stablehlo.concatenate %246, %214, %248, dim = 0 {mhlo.sharding = "{devices=[1,4,8]<=[8,4]T(1,0)}"} : (tensor<1x2032x4080xf64>, tensor<128x2032x4080xf64>, tensor<1x2032x4080xf64>) -> tensor<130x2032x4080xf64> loc(#loc1194)

%573 = stablehlo.concatenate %571, %260, %572, dim = 0 {mhlo.sharding = "{devices=[1,4,8]<=[8,4]T(1,0)}"} : (tensor<1x1x4080xf64>, tensor<128x1x4080xf64>, tensor<1x1x4080xf64>) -> tensor<130x1x4080xf64> loc(#loc1190) // <-- not needed

%576 = stablehlo.concatenate %574, %262, %575, dim = 0 {mhlo.sharding = "{devices=[1,4,8]<=[8,4]T(1,0)}"} : (tensor<1x1x4080xf64>, tensor<128x1x4080xf64>, tensor<1x1x4080xf64>) -> tensor<130x1x4080xf64> loc(#loc1189) // <-- not needed

%577 = stablehlo.concatenate %573, %254, %576, dim = 1 {mhlo.sharding = "{devices=[1,4,8]<=[8,4]T(1,0)}"} : (tensor<130x1x4080xf64>, tensor<130x2032x4080xf64>, tensor<130x1x4080xf64>) -> tensor<130x2034x4080xf64> loc(#loc1162)

return %iterArg_102 = %577
```

Start with the two StableHLO examples in the issue and trace how the loop-carried tensors produce the shown slice and concatenate operations. Determine how the access analysis should recognize equivalent regions and remove redundant operations; done means the loop no longer contains the unnecessary slices and concats while preserving the demonstrated tensor shapes and results.

Written by the indexing model from the issue text.