code: def stack_in_memory(data, iterations):
      """
      Stack `data` `iterations` times on the batch axis.
      Args:
          data (Tensor): Data to stack
          iterations (int): Number of time to stack.
  
      Raises:
          RuntimeError when CUDA is out of memory.
  
      Returns:
          Tensor with shape [batch_size * iterations, ...]
      """
      input_shape = data.size()
      batch_size = input_shape[0]
      try:
          data = torch.stack([data] * iterations)
      except RuntimeError as e:
          raise RuntimeError(
              """CUDA ran out of memory while BaaL tried to replicate data. See the exception above.
          Use `replicate_in_memory=False` in order to reduce the memory requirements.
          Note that there will be some speed trade-offs"""
          ) from e
      data = data.view(batch_size * iterations, *input_shape[1:])
      return data
  
imports: import numpy
  import torch
  from scipy.special import softmax, expit
label_desc: Use image node_id 2 for calling a global function with example usage: stack_in_memory(data, iterations) and returns: data