code: def compute_negative_log_likelihood_loss(input, target, weight=None, reduction="mean", ignore_index=None):  # type: ignore
      input_shape = input.shape
      if len(input_shape) == 1:
          raise RuntimeError("Unsupported shape")
  
      target_shape = target.shape
      N = input_shape[0]
      C = input_shape[1]
  
      # initialize the positional weights when required
      gather_weight = None
      if weight is not None:
          # setting mode='clip' to deal with ignore_index > C or < 0 cases.
          # when the target value is > C or < 0, it doesn't matter which value we are
          # taking in gather_weight, since it will be set to 0 in the following if-block
          # use np.int32 to make it compatible with x86 machines
          gather_weight = np.take(
              weight, np.array(target, dtype=np.int32), mode="clip"
          )
          # set `ignore_index`'s loss weight to 0.
          # The loss tensor will be multiplied by this weight tensor,
          # so `ingore_index`'s loss value will be eliminated.
          if ignore_index is not None:
              gather_weight = np.where(
                  target == ignore_index, 0, gather_weight
              ).astype(dtype=np.float32)
      elif ignore_index is not None:
          gather_weight = np.where(target == ignore_index, 0, 1).astype(
              dtype=np.float32
          )
  
      # if input is 4-d and above, make it 3-d
      if len(input_shape) != 3:
          input = input.reshape((N, C, -1))
          target = target.reshape((N, -1))
  
      # Get a dimension from the reshaped input.
      # If the original input shape is [N, C, H, W],
      # the D here should be H * W because we reshape
      # [N, C, H, W] to [N, C, H * W].
      D = input.shape[2]
      neg_gather_element_input = np.zeros((N, D), dtype=np.float32)
      for i in range(N):
          for d in range(D):
              if target[i][d] != ignore_index:
                  neg_gather_element_input[i][d] = -input[i][
                      target[i][d]
                  ][d]
  
      loss = neg_gather_element_input
  
      # if the input was 4-d or above reshape to the right shape
      if len(input_shape) != 3:
          loss = loss.reshape(target_shape)
  
      # apply the weights when required
      if gather_weight is not None:
          loss = gather_weight * loss
          if reduction == "mean":
              loss = loss.sum() / gather_weight.sum()
              return loss
  
      if reduction == "mean":
          loss = np.mean(loss)
      elif reduction == "sum":
          loss = np.sum(loss)
      return loss
  
imports: import numpy
  import onnx
  from onnx.backend.test.case.base import Base
  from onnx.backend.test.case.node import expect
label_desc: Use image node_id 1 for calling a global function with example usage: compute_negative_log_likelihood_loss(input, target, weight, reduction, ignore_index) and returns: loss, loss