//   %1 = "mhlo.iota"() {iota_dimension = 0 : i64}
//   %2 = "mhlo.iota"() {iota_dimension = 1 : i64}
//   %3 = "mhlo.compare"(%1, %2) {comparison_direction = "EQ"}
//   %4 = mhlo.constant dense<0.000000e+00> : tensor<f32>
//   %5 = "mhlo.broadcast"(%4)
//   %6 = "mhlo.select"(%3, %input, %5)
//   %7 = "mhlo.reduce"(%6, %4) ({
//   ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):

```
  indices = [0, 2, -1, 1]
  depth = 3
  on_value = 5.0
  off_value = 0.0
  axis = -1
```

Then output is `[4 x 3]`:
```
output =
  [5.0 0.0 0.0]  // one_hot(0)
  [0.0 0.0 5.0]  // one_hot(2)
  [0.0 0.0 0.0]  // one_hot(-1)
  [0.0 5.0 0.0]  // one_hot(1)
```

Suppose that
```
  indices = [0, 2, -1, 1]
  depth = 3
  on_value = 0.0