becomes part of `outputs[partitions[js]]`.  The slices with `partitions[js] = i`
are placed in `outputs[i]` in lexicographic order of `js`, and the first
dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`.
In detail,

```python
    outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]

    outputs[i] = pack([data[js, ...] for js if partitions[js] == i])

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteTensorHandle(retvals[0]);
  float output[2] = {0};
  EXPECT_EQ(sizeof(output), TF_TensorByteSize(t));
  memcpy(&output[0], TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);
  EXPECT_EQ(1, output[0]);
  EXPECT_EQ(3, output[1]);
  TFE_DeleteContext(ctx);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);

// `tfl.batch_matmul` when it accepts uniform quantized tensors.
//
// StableHLO Quantizer output:
//   * input: per-tensor qi8
//   * filter: per-channel qi8 for non-batching op, per-tensor for batching op.
//   * output: per-tensor qi32
// JAX Quantizer output:
//   * input: per-tensor qi8
//   * filter: per-channel qi8
//   * output: per-tensor qi8
//
// Conditions for the `tfl.batch_matmul` conversion:

    key: "__saved_model_init_op"
    value {
      outputs {
        key: "__saved_model_init_op"
        value {
          name: "NoOp"
          tensor_shape {
            unknown_rank: true
          }
        }
      }
    }
  }
  signature_def {
    key: "serving_default"
    value {
      outputs {
        key: "output_0"
        value {
          name: "PartitionedCall:0"

  outputs.reserve(o.outputs.size());
  for (const auto &outputs_ : o.outputs) { outputs.emplace_back((outputs_) ? new tflite::TensorMapT(*outputs_) : nullptr); }
}

inline SignatureDefT &SignatureDefT::operator=(SignatureDefT o) FLATBUFFERS_NOEXCEPT {
  std::swap(inputs, o.inputs);
  std::swap(outputs, o.outputs);
  std::swap(signature_key, o.signature_key);