void Range(vector<int32_t>* data, int32_t start, int32_t end,
           int32_t step = 1) {
  for (int32_t i = start; i < end; i += step) {
    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.
void GetDims(const TF_Tensor* t, int64_t* out_dims) {
  int num_dims = TF_NumDims(t);
  for (int i = 0; i < num_dims; i++) {
    out_dims[i] = TF_Dim(t, i);
  }
}

                absl::Span<AbstractTensorHandle*> outputs, bool use_function);

Status BuildImmediateExecutionContext(bool use_tfrt, AbstractContext** ctx);

// Return a tensor handle with given type, values and dimensions.
template <class T, TF_DataType datatype>
Status TestTensorHandleWithDims(AbstractContext* ctx, const T* data,
                                const int64_t* dims, int num_dims,

  if (num_dims > TensorShape::MaxDimensions()) {
    return errors::InvalidArgument("Value specified for `", attr_name, "` has ",
                                   num_dims,
                                   " dimensions which is over the limit of ",
                                   TensorShape::MaxDimensions(), ".");
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {

  bool valid = true;
  int64_t expected_stride = 1;
  for (int i = ndim - 1; i >= 0; --i) {
    // Empty tensors are always compact regardless of strides.
    if (shape_arr[i] == 0) return true;
    // Note that dimensions with size=1 can have any stride.
    if (shape_arr[i] != 1 && stride_arr[i] != expected_stride) {
      valid = false;
    }
    expected_stride *= shape_arr[i];
  }
  return valid;
}
}  // namespace

  DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape = shape_list->items[index];
  DCHECK(shape.dims == nullptr) << "Shape at " << index << " is already set!";
  DCHECK(num_dims >= 0) << "Number of dimensions cannot be negative!";
  shape.num_dims = num_dims;
  shape.dims = new int64_t[num_dims];
  memcpy(shape.dims, dims, sizeof(int64_t) * num_dims);
}

        control flow.
    *   Add `RaggedTensor.numpy()`.
    *   Update `RaggedTensor.__getitem__` to preserve uniform dimensions & allow
        indexing into uniform dimensions.
    *   Update `tf.expand_dims` to always insert the new dimension as a
        non-ragged dimension.
    *   Update `tf.embedding_lookup` to use `partition_strategy` and `max_norm`
        when `ids` is ragged.

        InvalidArgument("Node ", node->name(), " was not found in the graph");
    return -1;
  }

  tensorflow::shape_inference::ShapeHandle shape = ic->output(output.index);

  // Unknown rank means the number of dimensions is -1.
  if (!ic->RankKnown(shape)) {
    return -1;
  }

  return ic->Rank(shape);
}

void TF_GraphGetTensorShape(TF_Graph* graph, TF_Output output, int64_t* dims,

		This    string `json:"this"`    // This field carries assigned disk uuid.
		// Sets field carries the input disk order generated the first
		// time when fresh disks were supplied, it is a two dimensional
		// array second dimension represents list of disks used per set.
		Sets [][]string `json:"sets"`
		// Distribution algorithm represents the hashing algorithm
		// to pick the right set index for an object.

// are cached and refreshed after a given timeout.
//
// For metrics with a `bucket` dimension, a list of buckets argument is required
// to collect the metrics.
//
// It implements the prometheus.Collector interface for metric groups without a
// bucket dimension. For metric groups with a bucket dimension, use the
// `GetBucketCollector` method to get a `BucketCollector` that implements the

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