code: def _validate_inputs(
      room: torch.Tensor,
      source: torch.Tensor,
      mic_array: torch.Tensor,
  ):
      """Validate dimensions of input arguments, and normalize different kinds of absorption into the same dimension.
  
      Args:
          room (torch.Tensor): The size of the room. width, length (and height)
          source (torch.Tensor): Sound source coordinates. Tensor with dimensions `(dim,)`.
          mic_array (torch.Tensor): Microphone coordinates. Tensor with dimensions `(channel, dim)`.
      """
      if not (room.ndim == 1 and room.numel() == 3):
          raise ValueError(
              f"`room` must be a 1D Tensor with 3 elements. Found {room.shape}."
          )
      if not (source.ndim == 1 and source.numel() == 3):
          raise ValueError(
              f"`source` must be 1D Tensor with 3 elements. Found {source.shape}."
          )
      if not (mic_array.ndim == 2 and mic_array.shape[1] == 3):
          raise ValueError(
              f"`mic_array` must be a 2D Tensor with shape (num_channels, 3). Found {mic_array.shape}."
          )
  
file_path: audio/src/torchaudio/prototype/functional/_rir.py
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  import torchaudio
  from torch import Tensor
label_desc: Use image node_id 5 for calling a global function with example usage: _validate_inputs(room, source, mic_array) without return types