code: def one_hot(
      labels: torch.Tensor,
      num_classes: int,
      dtype: torch.dtype = torch.float,
      dim: int = 1,
  ) -> torch.Tensor:
      """
      For every value v in `labels`, the value in the output will be either 1 or 0. Each vector along the `dim`-th
      dimension has the "one-hot" format, i.e., it has a total length of `num_classes`,
      with a one and `num_class-1` zeros.
      Note that this will include the background label, thus a binary mask should be treated as having two classes.
  
      Args:
          labels: input tensor of integers to be converted into the 'one-hot' format. Internally `labels` will be
              converted into integers `labels.long()`.
          num_classes: number of output channels, the corresponding length of `labels[dim]` will be converted to
              `num_classes` from `1`.
          dtype: the data type of the output one_hot label.
          dim: the dimension to be converted to `num_classes` channels from `1` channel, should be non-negative number.
  
      Example:
  
      For a tensor `labels` of dimensions [B]1[spatial_dims], return a tensor of dimensions `[B]N[spatial_dims]`
      when `num_classes=N` number of classes and `dim=1`.
  
      .. code-block:: python
  
          from monai.networks.utils import one_hot
          import torch
  
          a = torch.randint(0, 2, size=(1, 2, 2, 2))
          out = one_hot(a, num_classes=2, dim=0)
          print(out.shape)  # torch.Size([2, 2, 2, 2])
  
          a = torch.randint(0, 2, size=(2, 1, 2, 2, 2))
          out = one_hot(a, num_classes=2, dim=1)
          print(out.shape)  # torch.Size([2, 2, 2, 2, 2])
  
      """
  
      # if `dim` is bigger, add singleton dim at the end
      if labels.ndim < dim + 1:
          shape = list(labels.shape) + [1] * (
              dim + 1 - len(labels.shape)
          )
          labels = torch.reshape(labels, shape)
  
      sh = list(labels.shape)
  
      if sh[dim] != 1:
          raise AssertionError(
              "labels should have a channel with length equal to one."
          )
  
      sh[dim] = num_classes
  
      o = torch.zeros(size=sh, dtype=dtype, device=labels.device)
      labels = o.scatter_(dim=dim, index=labels.long(), value=1)
  
      return labels
  
imports: from __future__ import annotations
  import io
  import re
  import warnings
  from collections import OrderedDict
  from collections.abc import Callable, Mapping, Sequence
  from contextlib import contextmanager
  from copy import deepcopy
  from typing import Any
  import numpy
  import torch
  import torch.nn
  from monai.apps.utils import get_logger
  from monai.config import PathLike
  from monai.utils.misc import ensure_tuple, save_obj, set_determinism
  from monai.utils.module import look_up_option, optional_import, pytorch_after
  from monai.utils.type_conversion import convert_to_dst_type, convert_to_tensor
label_desc: Use image node_id 4 for calling a global function with example usage: one_hot(labels, num_classes, dtype, dim) and returns: labels