code: def one_hot(x, num_classes, axis=-1, dtype="float32"):
      # Axis is the output axis. By default, PyTorch, outputs to last axis.
      # If axis is not last, change output to axis and shift remaining elements.
      x = convert_to_tensor(x, dtype=torch.long)
  
      # Torch one_hot does not natively handle negative values, so we add some
      # manual handling for negatives in the input to one_hot by using max(x, 0).
      # The output will have some invalid results, so we set them back to 0 using
      # `where` afterwards.
      output = tnn.one_hot(maximum(x, 0), num_classes)
      output = where(expand_dims(x, axis=-1) >= 0, output, 0)
      output = convert_to_tensor(output, dtype=dtype)
      dims = output.dim()
      if axis != -1 and axis != dims:
          new_axes_order = list(range(dims))
          new_axes_order[axis] = -1  # Shifts output to axis positon
          # Shift remaining axes with offset by 1 since output moved to `axis`.
          for ax in range(axis + 1, dims):
              new_axes_order[ax] -= 1
          output = output.permute(new_axes_order)
      return output
  
imports: import torch
  import torch.nn.functional
  import tree
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_padding_args_for_torch
  from keras.backend.config import epsilon
  from keras.backend.torch.core import cast
  from keras.backend.torch.core import convert_to_tensor
  from keras.backend.torch.core import get_device
  from keras.backend.torch.numpy import expand_dims
  from keras.backend.torch.numpy import maximum
  from keras.backend.torch.numpy import where
  from keras.utils.argument_validation import standardize_tuple
label_desc: Use image node_id 28 for calling a global function with example usage: one_hot(x, num_classes, axis, dtype) and returns: output