long chain of self additions to mul

```mlir
module @jit_fn attributes {mhlo.num_partitions = 1 : i32, mhlo.num_replicas = 1 : i32} {
func.func public @main(%arg0: tensor<1000x1000xf32>) -> (tensor<1000x1000xf32> {jax.result_info = "result"}) {
%cst = stablehlo.constant dense<0.000000e+00> : tensor
%0 = stablehlo.reduce(%arg0 init: %cst) applies stablehlo.add across dimensions = [0, 1] : (tensor<1000x1000xf32>, tensor) -> tensor
%1 = stablehlo.broadcast_in_dim %0, dims = [] : (tensor) -> tensor<1000x1000xf32>
%2 = stablehlo.add %1, %arg0 : tensor<1000x1000xf32>
%3 = stablehlo.add %2, %arg0 : tensor<1000x1000xf32>
%4 = stablehlo.add %3, %arg0 : tensor<1000x1000xf32>
%5 = stablehlo.add %4, %arg0 : tensor<1000x1000xf32>
%6 = stablehlo.add %5, %arg0 : tensor<1000x1000xf32>
%7 = stablehlo.add %6, %arg0 : tensor<1000x1000xf32>
%8 = stablehlo.add %7, %arg0 : tensor<1000x1000xf32>
%9 = stablehlo.add %8, %arg0 : tensor<1000x1000xf32>
%10 = stablehlo.add %9, %arg0 : tensor<1000x1000xf32>
return %10 : tensor<1000x1000xf32>
}
}
module @jit_fn attributes {mhlo.num_partitions = 1 : i32, mhlo.num_replicas = 1 : i32} {
func.func public @main(%arg0: tensor<1000x1000xf32>) -> (tensor<1000x1000xf32> {jax.result_info = "result"}) {
%cst = stablehlo.constant dense<0.000000e+00> : tensor
%0 = stablehlo.reduce(%arg0 init: %cst) applies stablehlo.add across dimensions = [0, 1] : (tensor<1000x1000xf32>, tensor) -> tensor
%1 = stablehlo.broadcast_in_dim %0, dims = [] : (tensor) -> tensor<1000x1000xf32>
%2 = stablehlo.add %1, %arg0 : tensor<1000x1000xf32>
%3 = stablehlo.add %arg0, %arg0 : tensor<1000x1000xf32>
%4 = stablehlo.add %3, %3 : tensor<1000x1000xf32>
%5 = stablehlo.add %4, %4 : tensor<1000x1000xf32>
%6 = stablehlo.add %2, %5 : tensor<1000x1000xf32>
return %6 : tensor<1000x1000xf32>
}
}
```

Start with the two MLIR modules in the issue and compare the long chain of additions with the shorter self-addition chain. Determine which transformation or behavior is unexpected and identify the relevant compiler entry point before defining a regression test. Done means the behavior is explained and covered by a focused test.

Written by the indexing model from the issue text.