code: def seq_to_spatial(
      sequence_tensor: torch.Tensor,
      desired_height: int,
      desired_width: int,
  ):
      """Takes a torch tensor of shape (sequence_length, batch_size, num_features)
      as used and outputted by Transformers and creates a view of shape
      (batch_size, num_features, height, width) as used and outputted by CNNs.
      In other words, rearranges the dimension ordering and rolls
      sequence_length into (height,width). height*width must equal
      the sequence length of the input sequence.
  
      Args:
          sequence_tensor: sequence tensor of shape (sequence_length, batch_size, num_features) (required).
          desired_height: the height into which the sequence length should be rolled into (required).
          desired_width: the width into which the sequence length should be rolled into (required).
  
      """
      original_size = sequence_tensor.size()
  
      batch_size = original_size[SEQUENCE_BATCH_DIMENSION]
      num_channels = original_size[SEQUENCE_FEATURE_DIMENSION]
  
      permuted_tensor = sequence_tensor.permute(
          SEQUENCE_BATCH_DIMENSION,
          SEQUENCE_FEATURE_DIMENSION,
          SEQUENCE_LENGTH_DIMENSION,
      )
  
      spatial_tensor = permuted_tensor.view(
          batch_size, num_channels, desired_height, desired_width
      )
  
      return spatial_tensor
  
label_desc: Use image node_id 2 for calling a global function with example usage: seq_to_spatial(sequence_tensor, desired_height, desired_width) and returns: spatial_tensor