(TFL_MirrorPadOp $arg0, $arg1, (TFL_GetMirrorPaddingType $mode))>;

def LegalizeSparseToDense : Pat<
  (TF_SparseToDenseOp $sparse_indices, $output_shape, $sparse_values,
    $default_value, $validate_indices),
  (TFL_SparseToDenseOp $sparse_indices, $output_shape, $sparse_values,
    $default_value)>;

def LegalizeUnique : Pat<(TF_UniqueOp $arg0),(TFL_UniqueOp $arg0)>;

```
# If sparse_indices is scalar
dense[i] = (i == sparse_indices ? sparse_values : default_value)

# If sparse_indices is a vector, then for each i
dense[sparse_indices[i]] = sparse_values[i]

# If sparse_indices is an n by d matrix, then for each i in [0, n)
dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
```

          number_attr: "Ndense"
        }
        input_arg {
          name: "dense_defaults"
          type_list_attr: "Tdense"
        }
        output_arg {
          name: "sparse_indices"
          type: DT_INT64
          number_attr: "Nsparse"
        }
        output_arg {
          name: "sparse_values"
          type_list_attr: "sparse_types"
        }
        output_arg {

    Variadic<TF_StrTensor>:$dense_keys,
    Variadic<TensorOf<[TF_Float32, TF_Int64, TF_Str]>>:$dense_defaults,

    TF_ShapeAttrArray:$dense_shapes
  );

  let results = (outs
    Variadic<TF_Int64Tensor>:$sparse_indices,                           // len(sparse_types)
    Variadic<TensorOf<[TF_Float32, TF_Int64, TF_Str]>>:$sparse_values,  // len(sparse_types)

```
# If sparse_indices is scalar
dense[i] = (i == sparse_indices ? sparse_values : default_value)

# If sparse_indices is a vector, then for each i
dense[sparse_indices[i]] = sparse_values[i]

# If sparse_indices is an n by d matrix, then for each i in [0, n)
dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
```

                          << "the length of attribute 'sparse_types'";
  }
  if (op.getSparseIndices().size() != sparse_types_count) {
    return op.emitError() << "output 'sparse_indices' should have same length "
                          << "as attribute 'sparse_types'";
  }
  if (op.getSparseShapes().size() != sparse_types_count) {

  // expected-error @+1 {{output 'sparse_indices' should have same length as attribute 'sparse_types'}}