prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Localizer train cfg & test cfg ?
  
  
  </issue>
  <code>
  [start of configs/_base_/models/bsn_tem.py]
  1 # model settings
  2 model = dict(
  3     type='TEM',
  4     temporal_dim=100,
  5     boundary_ratio=0.1,
  6     tem_feat_dim=400,
  7     tem_hidden_dim=512,
  8     tem_match_threshold=0.5)
  9 # model training and testing settings
  10 train_cfg = None
  11 test_cfg = dict(average_clips='score')
  12
  [end of configs/_base_/models/bsn_tem.py]
  [start of configs/_base_/models/bsn_pem.py]
  1 # model settings
  2 model = dict(
  3     type='PEM',
  4     pem_feat_dim=32,
  5     pem_hidden_dim=256,
  6     pem_u_ratio_m=1,
  7     pem_u_ratio_l=2,
  8     pem_high_temporal_iou_threshold=0.6,
  9     pem_low_temporal_iou_threshold=0.2,
  10     soft_nms_alpha=0.75,
  11     soft_nms_low_threshold=0.65,
  12     soft_nms_high_threshold=0.9,
  13     post_process_top_k=100)
  14 # model training and testing settings
  15 train_cfg = None
  16 test_cfg = dict(average_clips='score')
  17
  [end of configs/_base_/models/bsn_pem.py]
  [start of configs/_base_/models/bmn_400x100.py]
  1 # model settings
  2 model = dict(
  3     type='BMN',
  4     temporal_dim=100,
  5     boundary_ratio=0.5,
  6     num_samples=32,
  7     num_samples_per_bin=3,
  8     feat_dim=400,
  9     soft_nms_alpha=0.4,
  10     soft_nms_low_threshold=0.5,
  11     soft_nms_high_threshold=0.9,
  12     post_process_top_k=100)
  13 # model training and testing settings
  14 train_cfg = None
  15 test_cfg = dict(average_clips='score')
  16
  [end of configs/_base_/models/bmn_400x100.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/configs/_base_/models/bmn_400x100.py b/configs/_base_/models/bmn_400x100.py
  --- a/configs/_base_/models/bmn_400x100.py
  +++ b/configs/_base_/models/bmn_400x100.py
  @@ -10,6 +10,3 @@
       soft_nms_low_threshold=0.5,
       soft_nms_high_threshold=0.9,
       post_process_top_k=100)
  -# model training and testing settings
  -train_cfg = None
  -test_cfg = dict(average_clips='score')
  diff --git a/configs/_base_/models/bsn_pem.py b/configs/_base_/models/bsn_pem.py
  --- a/configs/_base_/models/bsn_pem.py
  +++ b/configs/_base_/models/bsn_pem.py
  @@ -11,6 +11,3 @@
       soft_nms_low_threshold=0.65,
       soft_nms_high_threshold=0.9,
       post_process_top_k=100)
  -# model training and testing settings
  -train_cfg = None
  -test_cfg = dict(average_clips='score')
  diff --git a/configs/_base_/models/bsn_tem.py b/configs/_base_/models/bsn_tem.py
  --- a/configs/_base_/models/bsn_tem.py
  +++ b/configs/_base_/models/bsn_tem.py
  @@ -6,6 +6,3 @@
       tem_feat_dim=400,
       tem_hidden_dim=512,
       tem_match_threshold=0.5)
  -# model training and testing settings
  -train_cfg = None
  -test_cfg = dict(average_clips='score')
  
verification_info: {"golden_diff": "diff --git a/configs/_base_/models/bmn_400x100.py b/configs/_base_/models/bmn_400x100.py\n--- a/configs/_base_/models/bmn_400x100.py\n+++ b/configs/_base_/models/bmn_400x100.py\n@@ -10,6 +10,3 @@\n     soft_nms_low_threshold=0.5,\n     soft_nms_high_threshold=0.9,\n     post_process_top_k=100)\n-# model training and testing settings\n-train_cfg = None\n-test_cfg = dict(average_clips='score')\ndiff --git a/configs/_base_/models/bsn_pem.py b/configs/_base_/models/bsn_pem.py\n--- a/configs/_base_/models/bsn_pem.py\n+++ b/configs/_base_/models/bsn_pem.py\n@@ -11,6 +11,3 @@\n     soft_nms_low_threshold=0.65,\n     soft_nms_high_threshold=0.9,\n     post_process_top_k=100)\n-# model training and testing settings\n-train_cfg = None\n-test_cfg = dict(average_clips='score')\ndiff --git a/configs/_base_/models/bsn_tem.py b/configs/_base_/models/bsn_tem.py\n--- a/configs/_base_/models/bsn_tem.py\n+++ b/configs/_base_/models/bsn_tem.py\n@@ -6,6 +6,3 @@\n     tem_feat_dim=400,\n     tem_hidden_dim=512,\n     tem_match_threshold=0.5)\n-# model training and testing settings\n-train_cfg = None\n-test_cfg = dict(average_clips='score')\n", "issue": "Localizer train cfg & test cfg ?\n\n", "before_files": [{"content": "# model settings\nmodel = dict(\n    type='TEM',\n    temporal_dim=100,\n    boundary_ratio=0.1,\n    tem_feat_dim=400,\n    tem_hidden_dim=512,\n    tem_match_threshold=0.5)\n# model training and testing settings\ntrain_cfg = None\ntest_cfg = dict(average_clips='score')\n", "path": "configs/_base_/models/bsn_tem.py"}, {"content": "# model settings\nmodel = dict(\n    type='PEM',\n    pem_feat_dim=32,\n    pem_hidden_dim=256,\n    pem_u_ratio_m=1,\n    pem_u_ratio_l=2,\n    pem_high_temporal_iou_threshold=0.6,\n    pem_low_temporal_iou_threshold=0.2,\n    soft_nms_alpha=0.75,\n    soft_nms_low_threshold=0.65,\n    soft_nms_high_threshold=0.9,\n    post_process_top_k=100)\n# model training and testing settings\ntrain_cfg = None\ntest_cfg = dict(average_clips='score')\n", "path": "configs/_base_/models/bsn_pem.py"}, {"content": "# model settings\nmodel = dict(\n    type='BMN',\n    temporal_dim=100,\n    boundary_ratio=0.5,\n    num_samples=32,\n    num_samples_per_bin=3,\n    feat_dim=400,\n    soft_nms_alpha=0.4,\n    soft_nms_low_threshold=0.5,\n    soft_nms_high_threshold=0.9,\n    post_process_top_k=100)\n# model training and testing settings\ntrain_cfg = None\ntest_cfg = dict(average_clips='score')\n", "path": "configs/_base_/models/bmn_400x100.py"}]}