The compiler is very smart on static shape inference that can generate
partially dynamic shape during the lowering. It makes data-tiling fusion
very struggle because they are expected to be dynamic shape but some
dimensions are inferred to static values in Stream
AnnotateDispatchAssumptions pass. Because it will lead to `tensor.cast
-> set_encoding -> tensor.cast` sequence in a dispatch, while we expect
the bindings have encoded tensor types. E.g.,

Input IR:

```mlir
%0 = iree_tensor_ext.dispatch_load ... tensor<?x?xi8>
%1 = set_encoding %0 : tensor<?x?xi8> -> tensor<?x?xi8, #encoding>
iree_tensor_ext.dispatch_store %1, ... tensor<?x?xi8, #encoding> ->
  ... tensor<?x?xi8, #encoding>
```

After annotation:

```mlir
%0 = iree_tensor_ext.dispatch_load ... tensor<4x?xi8>
%cast = tensor.cast %0 : tensor<4x?xi8> -> tensor<?x?xi8>
%1 = set_encoding %cast : tensor<?x?xi8> -> tensor<?x?xi8, #encoding>
%cast_0 = tensor.cast %1 : tensor<?x?xi8, #encoding> to tensor<4x5xi8>
iree_tensor_ext.dispatch_store %cast_0, ... tensor<4x5xi8> ->
  ... tensor<?x?xi8, #encoding>
```

It is hard to materialize the encodings when cast op is present.

Given that the original goal is testing dynamic shape, modifying the
input program is an easier fix.

The issue is observed from #21441.

Signed-off-by: hanhanW <hanhan0912@gmail.com>