return new ImmutableList<E>() {

      @Override
      public int size() {
        return axes.size();
      }

      @Override
      public E get(int axis) {
        checkElementIndex(axis, size());
        int axisIndex = getAxisIndexForProductIndex(index, axis);
        return axes.get(axis).get(axisIndex);
      }

      @Override
      boolean isPartialView() {
        return true;

    return new ImmutableList<E>() {

      @Override
      public int size() {
        return axes.size();
      }

      @Override
      public E get(int axis) {
        checkElementIndex(axis, size());
        int axisIndex = getAxisIndexForProductIndex(index, axis);
        return axes.get(axis).get(axisIndex);
      }

      @Override
      boolean isPartialView() {
        return true;

      } else {
        // Generalize differing axis lengths to "variable"/"unknown".
        for (int axis_index = 0; axis_index < combined_shape.dims();
             ++axis_index) {
          int64_t axis_length = combined_shape.dim_size(axis_index);
          if (axis_length != component_shape.dim_size(axis_index)) {
            axis_length = -1;
          }
          TF_RETURN_IF_ERROR(

// Return a 1-D INT32 tensor containing a single value 1.
TFE_TensorHandle* TestAxisTensorHandle(TFE_Context* ctx);

// Return an op taking minimum of `input` long `axis` dimension.
TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input,
              TFE_TensorHandle* axis);

// If there is a device of type `device_type`, returns true
// and sets 'device_name' accordingly.
// `device_type` must be either "GPU" or "TPU".

TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input,
              TFE_TensorHandle* axis) {
  TF_Status* status = TF_NewStatus();

  TFE_Op* op = TFE_NewOp(ctx, "Min", status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddInput(op, input, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddInput(op, axis, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

      int adjust = size() - 1;
      for (int i = 0; i < axes.size(); i++) {
        adjust *= 31;
        adjust = ~~adjust;
        // in GWT, we have to deal with integer overflow carefully
      }
      int hash = 1;
      for (Set<E> axis : axes) {
        hash = 31 * hash + (size() / axis.size() * axis.hashCode());

        hash = ~~hash;
      }
      hash += adjust;

`tf.reduce_join`: `reduction_indices` becomes `axis` * `tf.reduce_logsumexp`:
`reduction_indices` becomes `axis` * `tf.reduce_max`: `reduction_indices`
becomes `axis` * `tf.reduce_mean`: `reduction_indices` becomes `axis` *
`tf.reduce_min`: `reduction_indices` becomes `axis` * `tf.reduce_prod`:
`reduction_indices` becomes `axis` * `tf.reduce_sum`: `reduction_indices`

  TFE_TensorHandle* axis = TestAxisTensorHandle(ctx);
  TFE_Op* minOp = MinOp(ctx, input, axis);
  TFE_TensorHandle* retvals[1] = {nullptr};
  int num_retvals = 1;
  TFE_Execute(minOp, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteOp(minOp);
  TFE_DeleteTensorHandle(input);
  TFE_DeleteTensorHandle(axis);
  ASSERT_EQ(1, num_retvals);

// this context.
TF_CAPI_EXPORT extern void TFE_ContextDisableGraphCollection(TFE_Context* ctx);

// TODO(fishx): Move these monitoring APIs into a separate file.
// -----------------------------------------------------------------------------
// Monitoring Counter APIs.
// These APIs de-templated monitoring Counter for swig.

typedef struct TFE_MonitoringCounterCell TFE_MonitoringCounterCell;

  // Make a placeholder operation.
  TF_Operation* feed = Placeholder(graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Make a constant operation with the scalar "0", for axis.
  TF_Operation* one = ScalarConst(0, graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Create a session for this graph.
  CSession csession(graph, s, use_XLA);