prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Unused import of math in time_sequence_prediction example
  The generate_sine_wave.py module imports math on the first line, but doesn't use it. This import should be removed.
  
  </issue>
  <code>
  [start of time_sequence_prediction/generate_sine_wave.py]
  1 import math
  2 import numpy as np
  3 import torch
  4 T = 20
  5 L = 1000
  6 N = 100
  7 np.random.seed(2)
  8 x = np.empty((N, L), 'int64')
  9 x[:] = np.array(range(L)) + np.random.randint(-4*T, 4*T, N).reshape(N, 1)
  10 data = np.sin(x / 1.0 / T).astype('float64')
  11 torch.save(data, open('traindata.pt', 'wb'))
  12 
  13
  [end of time_sequence_prediction/generate_sine_wave.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/time_sequence_prediction/generate_sine_wave.py b/time_sequence_prediction/generate_sine_wave.py
  --- a/time_sequence_prediction/generate_sine_wave.py
  +++ b/time_sequence_prediction/generate_sine_wave.py
  @@ -1,12 +1,13 @@
  -import math
   import numpy as np
   import torch
  +
  +np.random.seed(2)
  +
   T = 20
   L = 1000
   N = 100
  -np.random.seed(2)
  +
   x = np.empty((N, L), 'int64')
  -x[:] = np.array(range(L)) + np.random.randint(-4*T, 4*T, N).reshape(N, 1)
  +x[:] = np.array(range(L)) + np.random.randint(-4 * T, 4 * T, N).reshape(N, 1)
   data = np.sin(x / 1.0 / T).astype('float64')
   torch.save(data, open('traindata.pt', 'wb'))
  -
  
verification_info: {"golden_diff": "diff --git a/time_sequence_prediction/generate_sine_wave.py b/time_sequence_prediction/generate_sine_wave.py\n--- a/time_sequence_prediction/generate_sine_wave.py\n+++ b/time_sequence_prediction/generate_sine_wave.py\n@@ -1,12 +1,13 @@\n-import math\n import numpy as np\n import torch\n+\n+np.random.seed(2)\n+\n T = 20\n L = 1000\n N = 100\n-np.random.seed(2)\n+\n x = np.empty((N, L), 'int64')\n-x[:] = np.array(range(L)) + np.random.randint(-4*T, 4*T, N).reshape(N, 1)\n+x[:] = np.array(range(L)) + np.random.randint(-4 * T, 4 * T, N).reshape(N, 1)\n data = np.sin(x / 1.0 / T).astype('float64')\n torch.save(data, open('traindata.pt', 'wb'))\n-\n", "issue": "Unused import of math in time_sequence_prediction example\nThe generate_sine_wave.py module imports math on the first line, but doesn't use it. This import should be removed.\n", "before_files": [{"content": "import math\nimport numpy as np\nimport torch\nT = 20\nL = 1000\nN = 100\nnp.random.seed(2)\nx = np.empty((N, L), 'int64')\nx[:] = np.array(range(L)) + np.random.randint(-4*T, 4*T, N).reshape(N, 1)\ndata = np.sin(x / 1.0 / T).astype('float64')\ntorch.save(data, open('traindata.pt', 'wb'))\n\n", "path": "time_sequence_prediction/generate_sine_wave.py"}]}