code: def in_top_k(targets, predictions, k):
      targets = convert_to_tensor(targets).type(torch.int64)
      targets = targets[:, None]
      predictions = convert_to_tensor(predictions)
      topk_values = top_k(predictions, k).values
      targets_values = torch.take_along_dim(
          predictions, targets, dim=-1
      )
      mask = targets_values >= topk_values
      return torch.any(mask, axis=-1)
  
imports: import math
  import torch
  from keras.backend import standardize_dtype
  from keras.backend.torch.core import convert_to_tensor
  from keras.backend.torch.core import get_device
  from keras.backend.torch.numpy import pad
label_desc: Use image node_id 4 for calling a global function with example usage: in_top_k(targets, predictions, k) and returns: torch