ArrayRef<int64_t> contracting_dimensions) {
    const int64_t rank = type.getRank();
    for (const int64_t dim : batch_dimensions) {
      batch_dimensions_.axes.push_back(dim);
      batch_dimensions_.sizes.push_back(type.getDimSize(dim));
    }

    for (const int64_t dim : contracting_dimensions) {
      contracting_dimensions_.axes.push_back(dim);

// -> tfl.batch_matmul(mhlo.transpose(mhlo.reshape(operand)), ...).
// Note:
// 1) Reshape/transpose are inserted because tfl.BatchMatMul requires
// size(contracting_dimensions) = 1 and size(output_dim) = 1, whereas
// mhlo.dot_general has no such restriction.
// 2) Inserted mhlo.reshape/transpose will be legalized to tf.reshape/transpose
// in LegalizeHloToTf (then from tf to tfl later).

                    ArrayRef<int64_t> contracting_dimensions) {
    const int64_t rank = type.getRank();
    for (const int64_t dim : batch_dimensions) {
      batch_dimensions_.axes.push_back(dim);
      batch_dimensions_.sizes.push_back(type.getDimSize(dim));
    }

    for (const int64_t dim : contracting_dimensions) {
      contracting_dimensions_.axes.push_back(dim);

//   * filter: per-channel qi8
//   * output: per-tensor qi8
//
// Conditions for the `tfl.batch_matmul` conversion:
//   * size(batching_dimensions) <= 3 (TFLite support restriction)
//   * size(contracting_dimensions) = 1
//   * Input tensors are per-tensor uniform quantized (i8->f32)
//     tensors (full integer) with shape [..., r_x, c_x] or [..., c_x, r_x].
//   * The filter tensor is a per-tensor uniform quantized (i8->f32) tensor

  return %1 : tensor<1x2x3x5x!quant.uniform<i8:f32, 4.000000e+0>>
}
// Only support size(contracting_dimensions) == 1
// CHECK-LABEL: dot_general_upstream_srq_too_many_contractions
// CHECK: stablehlo.dot_general
// CHECK-NOT: tfl.batch_matmul

// -----