prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Remove unused module
  [`torchaudio._internal.misc_ops`](https://github.com/pytorch/audio/blob/b059f08742e70700ce4c92296a1131118f67a588/torchaudio/_internal/misc_ops.py) is a residue from refactoring of I/O features in the past releases. We can get rid of the whole module.
  
  
  
  </issue>
  <code>
  [start of torchaudio/_internal/misc_ops.py]
  1 from typing import Union, Callable
  2 
  3 import torch
  4 from torch import Tensor
  5 
  6 
  7 def normalize_audio(signal: Tensor, normalization: Union[bool, float, Callable]) -> None:
  8     """Audio normalization of a tensor in-place.  The normalization can be a bool,
  9     a number, or a callable that takes the audio tensor as an input. SoX uses
  10     32-bit signed integers internally, thus bool normalizes based on that assumption.
  11     """
  12 
  13     if not normalization:
  14         return
  15 
  16     if isinstance(normalization, bool):
  17         normalization = 1 << 31
  18 
  19     if isinstance(normalization, (float, int)):
  20         # normalize with custom value
  21         signal /= normalization
  22     elif callable(normalization):
  23         signal /= normalization(signal)
  24 
  25 
  26 def check_input(src: Tensor) -> None:
  27     if not torch.is_tensor(src):
  28         raise TypeError('Expected a tensor, got %s' % type(src))
  29     if src.is_cuda:
  30         raise TypeError('Expected a CPU based tensor, got %s' % type(src))
  31
  [end of torchaudio/_internal/misc_ops.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/torchaudio/_internal/misc_ops.py b/torchaudio/_internal/misc_ops.py
  deleted file mode 100644
  --- a/torchaudio/_internal/misc_ops.py
  +++ /dev/null
  @@ -1,30 +0,0 @@
  -from typing import Union, Callable
  -
  -import torch
  -from torch import Tensor
  -
  -
  -def normalize_audio(signal: Tensor, normalization: Union[bool, float, Callable]) -> None:
  -    """Audio normalization of a tensor in-place.  The normalization can be a bool,
  -    a number, or a callable that takes the audio tensor as an input. SoX uses
  -    32-bit signed integers internally, thus bool normalizes based on that assumption.
  -    """
  -
  -    if not normalization:
  -        return
  -
  -    if isinstance(normalization, bool):
  -        normalization = 1 << 31
  -
  -    if isinstance(normalization, (float, int)):
  -        # normalize with custom value
  -        signal /= normalization
  -    elif callable(normalization):
  -        signal /= normalization(signal)
  -
  -
  -def check_input(src: Tensor) -> None:
  -    if not torch.is_tensor(src):
  -        raise TypeError('Expected a tensor, got %s' % type(src))
  -    if src.is_cuda:
  -        raise TypeError('Expected a CPU based tensor, got %s' % type(src))
  
verification_info: {"golden_diff": "diff --git a/torchaudio/_internal/misc_ops.py b/torchaudio/_internal/misc_ops.py\ndeleted file mode 100644\n--- a/torchaudio/_internal/misc_ops.py\n+++ /dev/null\n@@ -1,30 +0,0 @@\n-from typing import Union, Callable\n-\n-import torch\n-from torch import Tensor\n-\n-\n-def normalize_audio(signal: Tensor, normalization: Union[bool, float, Callable]) -> None:\n-    \"\"\"Audio normalization of a tensor in-place.  The normalization can be a bool,\n-    a number, or a callable that takes the audio tensor as an input. SoX uses\n-    32-bit signed integers internally, thus bool normalizes based on that assumption.\n-    \"\"\"\n-\n-    if not normalization:\n-        return\n-\n-    if isinstance(normalization, bool):\n-        normalization = 1 << 31\n-\n-    if isinstance(normalization, (float, int)):\n-        # normalize with custom value\n-        signal /= normalization\n-    elif callable(normalization):\n-        signal /= normalization(signal)\n-\n-\n-def check_input(src: Tensor) -> None:\n-    if not torch.is_tensor(src):\n-        raise TypeError('Expected a tensor, got %s' % type(src))\n-    if src.is_cuda:\n-        raise TypeError('Expected a CPU based tensor, got %s' % type(src))\n", "issue": "Remove unused module\n[`torchaudio._internal.misc_ops`](https://github.com/pytorch/audio/blob/b059f08742e70700ce4c92296a1131118f67a588/torchaudio/_internal/misc_ops.py) is a residue from refactoring of I/O features in the past releases. We can get rid of the whole module.\r\n\r\n\n", "before_files": [{"content": "from typing import Union, Callable\n\nimport torch\nfrom torch import Tensor\n\n\ndef normalize_audio(signal: Tensor, normalization: Union[bool, float, Callable]) -> None:\n    \"\"\"Audio normalization of a tensor in-place.  The normalization can be a bool,\n    a number, or a callable that takes the audio tensor as an input. SoX uses\n    32-bit signed integers internally, thus bool normalizes based on that assumption.\n    \"\"\"\n\n    if not normalization:\n        return\n\n    if isinstance(normalization, bool):\n        normalization = 1 << 31\n\n    if isinstance(normalization, (float, int)):\n        # normalize with custom value\n        signal /= normalization\n    elif callable(normalization):\n        signal /= normalization(signal)\n\n\ndef check_input(src: Tensor) -> None:\n    if not torch.is_tensor(src):\n        raise TypeError('Expected a tensor, got %s' % type(src))\n    if src.is_cuda:\n        raise TypeError('Expected a CPU based tensor, got %s' % type(src))\n", "path": "torchaudio/_internal/misc_ops.py"}]}