code: def interpolate(
      x: torch.Tensor,
      size: Union[Sequence[int], int],
      /,
      *,
      mode: Literal[
          "linear",
          "bilinear",
          "trilinear",
          "nd",
          "nearest",
          "area",
          "nearest_exact",
          "tf_area",
          "tf_bicubic",
          "bicubic",
          "mitchellcubic",
          "lanczos3",
          "lanczos5",
          "gaussian",
      ] = "linear",
      scale_factor: Optional[Union[Sequence[int], int]] = None,
      recompute_scale_factor: Optional[bool] = None,
      align_corners: bool = False,
      antialias: bool = False,
      out: Optional[torch.Tensor] = None,
  ):
      if mode not in ["linear", "bilinear", "bicubic", "trilinear"]:
          align_corners = None
      return torch.nn.functional.interpolate(
          x,
          size=size,
          mode=mode,
          align_corners=align_corners,
          antialias=antialias,
          scale_factor=scale_factor,
          recompute_scale_factor=recompute_scale_factor,
      )
  
imports: from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import torch
  import math
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  from ivy.functional.ivy.layers import _handle_padding, _get_num_padded_values, _validate_max_pool_params, _depth_max_pooling_helper
  from ivy.functional.ivy.experimental.layers import _padding_ceil_mode, _broadcast_pooling_helper
label_desc: Use image node_id 20 for calling a global function with example usage: interpolate() and returns: torch