code: def logsumexp(x, axis=None, keepdims=False):
      x = convert_to_tensor(x)
      if axis is None:
          max_x = torch.max(x)
          return torch.log(torch.sum(torch.exp(x - max_x))) + max_x
  
      max_x = torch.amax(x, dim=axis, keepdim=True)
      result = (
          torch.log(
              torch.sum(torch.exp(x - max_x), dim=axis, keepdim=True)
          )
          + max_x
      )
      return torch.squeeze(result, dim=axis) if not keepdims else result
  
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 5 for calling a global function with example usage: logsumexp(x, axis, keepdims) and returns: unknown, unknown