[mlir][Linalg] Introduce linalg.pooling_min/max/sum op.

Summary:
Performs an N-D pooling operation similarly to the description in the TF
documentation:
https://www.tensorflow.org/api_docs/python/tf/nn/pool

Different from the description, this operation doesn't perform on batch and
channel. It only takes tensors of rank `N`.

```
  output[x[0], ..., x[N-1]] =
    REDUCE_{z[0], ..., z[N-1]}
      input[
            x[0] * strides[0] - pad_before[0] + dilation_rate[0]*z[0],
            ...
            x[N-1]*strides[N-1] - pad_before[N-1] + dilation_rate[N-1]*z[N-1]
            ],
```

The required optional arguments are:
  - strides: an i64 array specifying the stride (i.e. step) for window
    loops.
  - dilations: an i64 array specifying the filter upsampling/input
    downsampling rate
  - padding: an i64 array of pairs (low, high) specifying the number of
    elements to pad along a dimension.

If strides or dilations attributes are missing then the default value is
one for each of the input dimensions. Similarly, padding values are zero
for both low and high in each of the dimensions, if not specified.

Differential Revision: https://reviews.llvm.org/D76414

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