code: def to_float_tensor(numpy_array: np.ndarray) -> torch.Tensor:
      """
      Convert the numpy array to torch float tensor with dimension of NxCxHxW.
      Pytorch is not fully supporting uint8, so convert tensor to float if the
      numpy_array is uint8.
      Args:
          numpy_array (ndarray): of shape NxHxWxC, or HxWxC or HxW to
              represent an image. The array can be of type uint8 in range
              [0, 255], or floating point in range [0, 1] or [0, 255].
      Returns:
          float_tensor (tensor): converted float tensor.
      """
      assert isinstance(numpy_array, np.ndarray)
      assert len(numpy_array.shape) in (2, 3, 4)
  
      # Some of the input numpy array has negative strides. Pytorch currently
      # does not support negative strides, perform ascontiguousarray to
      # resolve the issue.
      float_tensor = torch.from_numpy(np.ascontiguousarray(numpy_array))
      if numpy_array.dtype in (np.uint8, np.int32, np.int64):
          float_tensor = float_tensor.float()
  
      if len(numpy_array.shape) == 2:
          # HxW -> 1x1xHxW.
          float_tensor = float_tensor[None, None, :, :]
      elif len(numpy_array.shape) == 3:
          # HxWxC -> 1xCxHxW.
          float_tensor = float_tensor.permute(2, 0, 1)
          float_tensor = float_tensor[None, :, :, :]
      elif len(numpy_array.shape) == 4:
          # NxHxWxC -> NxCxHxW
          float_tensor = float_tensor.permute(0, 3, 1, 2)
      else:
          raise NotImplementedError(
              "Unknow numpy_array dimension of {}".format(
                  float_tensor.shape
              )
          )
      return float_tensor
  
label_desc: Use image node_id 1 for calling a global function with example usage: to_float_tensor(numpy_array) and returns: float_tensor