code: def _adjust_coeff(
      coeffs: Union[float, torch.Tensor], name: str
  ) -> torch.Tensor:
      """Validates and converts absorption or scattering parameters to a tensor with appropriate shape
  
      Args:
          coeff (float or torch.Tensor): The absorption coefficients of wall materials.
  
              If the dtype is ``float``, the absorption coefficient is identical for all walls and
              all frequencies.
  
              If ``absorption`` is a 1D Tensor, the shape must be `(2*dim,)`,
              where the values represent absorption coefficients of ``"west"``, ``"east"``,
              ``"south"``, ``"north"``, ``"floor"``, and ``"ceiling"``, respectively.
  
              If ``absorption`` is a 2D Tensor, the shape must be `(7, 2*dim)`,
              where 7 represents the number of octave bands.
  
      Returns:
          (torch.Tensor): The expanded coefficient.
              The shape is `(1, 6)` for single octave band case, and
              `(7, 6)` for multi octave band case.
      """
      num_walls = 6
      if isinstance(coeffs, float):
          if coeffs < 0:
              raise ValueError(
                  f"`{name}` must be non-negative. Found: {coeffs}"
              )
          return torch.full((1, num_walls), coeffs)
      if isinstance(coeffs, Tensor):
          if torch.any(coeffs < 0):
              raise ValueError(
                  f"`{name}` must be non-negative. Found: {coeffs}"
              )
          if coeffs.ndim == 1:
              if coeffs.numel() != num_walls:
                  raise ValueError(
                      f"The shape of `{name}` must be ({num_walls},) when it is a 1D Tensor. "
                      f"Found the shape {coeffs.shape}."
                  )
              return coeffs.unsqueeze(0)
          if coeffs.ndim == 2:
              if coeffs.shape[1] != num_walls:
                  raise ValueError(
                      f"The shape of `{name}` must be (NUM_BANDS, {num_walls}) when it "
                      f"is a 2D Tensor. Found: {coeffs.shape}."
                  )
              return coeffs
      raise TypeError(f"`{name}` must be float or Tensor.")
  
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 4 for calling a global function with example usage: _adjust_coeff(coeffs, name) and returns: torch, coeffs, coeffs