SmallVector<int32_t> shape;
  shape.reserve(output_shape.size());

  bool needs_truncation = true;
  for (size_t dim_idx = 0; dim_idx < output_shape.size(); ++dim_idx) {
    int64_t dim = output_shape[dim_idx];
    if (truncate && needs_truncation && dim == 1) {
      continue;
    } else if (needs_truncation && dim != 1) {
      needs_truncation = false;
    }

  }

  const int64_t rank = input_type.getRank();

  IntegerAttr dim_int = ExtractSingleElementAsInteger(split_dim_attr);

  // "axis" operand could be a i64 tensor. Resolve it here.
  IntegerAttr dim_i32;
  if (failed(ConvertToI32Attr(dim_int, &dim_i32))) return failure();

  int dim = dim_i32.getInt();
  if (dim < 0) dim += rank;

  const SmallVector<Value, 4>& slice_outputs = SliceOutputs(

		return 0, err
	}
	if f.entries == nil {
		return 0, EINVAL
	}

	n := 0
	for f.dirIdx < len(f.entries) {
		entry := f.entries[f.dirIdx]
		l := 2 + len(entry)
		if l > len(buf) {
			break
		}
		buf[0] = byte(l)
		buf[1] = byte(l >> 8)
		copy(buf[2:], entry)
		buf = buf[l:]
		n += l
		f.dirIdx++
	}

	return n, nil
}

func setStat(st *Stat_t, jsSt js.Value) {

      dims, num_dims);
}

int TF_NumDims(const TF_Tensor* t) { return t->tensor->NumDims(); }

int64_t TF_Dim(const TF_Tensor* t, int dim_index) {
  return t->tensor->Dim(dim_index);
}

size_t TF_TensorByteSize(const TF_Tensor* t) { return t->tensor->ByteSize(); }

void* TF_TensorData(const TF_Tensor* t) { return t->tensor->Data(); }

			if err = checkCrossDeviceMounts(eps); err != nil {
				return nil, setupType, config.ErrInvalidErasureEndpoints(nil).Msg(err.Error())
			}

			for diskIdx := range eps {
				eps[diskIdx].SetPoolIndex(poolIdx)
				eps[diskIdx].SetSetIndex(setIdx)
				eps[diskIdx].SetDiskIndex(diskIdx)
			}

			endpoints = append(endpoints, eps...)
		}

		if len(endpoints) == 0 {

    tensor_tile_rank--;
  }

  std::map<int, int> dimension_to_splits_map;
  for (int dim_index = 0; dim_index < tensor_tile_rank; ++dim_index) {
    if (sharding.tile_assignment_dimensions(dim_index) > 1) {
      dimension_to_splits_map.emplace(
          dim_index, sharding.tile_assignment_dimensions(dim_index));
    }
  }

  if (dimension_to_splits_map.empty()) {

	if d.cmd == debugDumpMode {
		d.cm.ResetModeAndGranularity()
	}
}

func (d *dstate) BeginCounterDataFile(cdf string, cdr *decodecounter.CounterDataReader, dirIdx int) {
	dbgtrace(2, "visit counter data file %s dirIdx %d", cdf, dirIdx)
	if d.cmd == debugDumpMode {
		fmt.Printf("data file %s", cdf)
		if cdr.Goos() != "" {
			fmt.Printf(" GOOS=%s", cdr.Goos())
		}
		if cdr.Goarch() != "" {

TF_CAPI_EXPORT extern int TFE_TensorDebugInfoOnDeviceNumDims(
    TFE_TensorDebugInfo* debug_info);

// Returns the number of elements in dimension `dim_index`.
// Tensor representation on device can be transposed from its representation
// on host. The data contained in dimension `dim_index` on device
// can correspond to the data contained in another dimension in on-host
// representation. The dimensions are indexed using the standard TensorFlow

      CompileTimeConstantOperand<TF::XlaReduceWindowOp, 2, 3, 4, 5, 6>,
      // $dim_index
      CompileTimeConstantOperand<TF::XlaRemoveDynamicDimensionSizeOp, 1>,
      // $window_dimensions, $window_strides, $padding
      CompileTimeConstantOperand<TF::XlaSelectAndScatterOp, 1, 2, 3>,
      CompileTimeConstantOperand<TF::XlaSetBoundOp, 1>,  // $bound
      // $dim_index
      CompileTimeConstantOperand<TF::XlaSetDynamicDimensionSizeOp, 1>

  return num_elements;
}

int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index,
                            TF_Status* status) {
  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return -1;
  }

  int64_t dim = -1;
  status->status = tensorflow::unwrap(h)->Dim(dim_index, &dim);
  return dim;
}