prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Fix generating_index_arrays.test_numpy_diag_indices
  |   |   |
  |---|---|
  |paddle|<a href="https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582"><img src=https://img.shields.io/badge/-failure-red></a>
  |tensorflow|<a href="https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582"><img src=https://img.shields.io/badge/-failure-red></a>
  |torch|<a href="https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582"><img src=https://img.shields.io/badge/-failure-red></a>
  |numpy|<a href="https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582"><img src=https://img.shields.io/badge/-failure-red></a>
  |jax|<a href="https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582"><img src=https://img.shields.io/badge/-failure-red></a>
  
  
  </issue>
  <code>
  [start of ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py]
  1 import ivy
  2 from ivy.functional.frontends.numpy.func_wrapper import (
  3     to_ivy_arrays_and_back,
  4     outputs_to_numpy_arrays,
  5 )
  6 
  7 
  8 @to_ivy_arrays_and_back
  9 def indices(dimensions, dtype=int, sparse=False):
  10     dimensions = tuple(dimensions)
  11     N = len(dimensions)
  12     shape = (1,) * N
  13     if sparse:
  14         res = tuple()
  15     else:
  16         res = ivy.empty((N,) + dimensions, dtype=dtype)
  17     for i, dim in enumerate(dimensions):
  18         idx = ivy.arange(dim, dtype=dtype).reshape(shape[:i] + (dim,) + shape[i + 1 :])
  19         if sparse:
  20             res = res + (idx,)
  21         else:
  22             res[i] = idx
  23     return res
  24 
  25 
  26 # unravel_index
  27 @to_ivy_arrays_and_back
  28 def unravel_index(indices, shape, order="C"):
  29     ret = [x.astype("int64") for x in ivy.unravel_index(indices, shape)]
  30     return tuple(ret)
  31 
  32 
  33 @outputs_to_numpy_arrays
  34 def diag_indices(n, ndim=2):
  35     idx = ivy.arange(n, dtype=int)
  36     return (idx,) * ndim
  37 
  38 
  39 @to_ivy_arrays_and_back
  40 def tril_indices(n, k=0, m=None):
  41     return ivy.tril_indices(n, m, k)
  42
  [end of ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.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/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py b/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py
  --- a/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py
  +++ b/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py
  @@ -30,10 +30,12 @@
       return tuple(ret)
   
   
  -@outputs_to_numpy_arrays
  +@to_ivy_arrays_and_back
   def diag_indices(n, ndim=2):
  -    idx = ivy.arange(n, dtype=int)
  -    return (idx,) * ndim
  +    idx = ivy.arange(n)
  +    res = ivy.array((idx,) * ndim)
  +    res = tuple(res.astype("int64"))
  +    return res
   
   
   @to_ivy_arrays_and_back
  
verification_info: {"golden_diff": "diff --git a/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py b/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py\n--- a/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py\n+++ b/ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py\n@@ -30,10 +30,12 @@\n     return tuple(ret)\n \n \n-@outputs_to_numpy_arrays\n+@to_ivy_arrays_and_back\n def diag_indices(n, ndim=2):\n-    idx = ivy.arange(n, dtype=int)\n-    return (idx,) * ndim\n+    idx = ivy.arange(n)\n+    res = ivy.array((idx,) * ndim)\n+    res = tuple(res.astype(\"int64\"))\n+    return res\n \n \n @to_ivy_arrays_and_back\n", "issue": "Fix generating_index_arrays.test_numpy_diag_indices\n|   |   |\r\n|---|---|\r\n|paddle|<a href=\"https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582\"><img src=https://img.shields.io/badge/-failure-red></a>\r\n|tensorflow|<a href=\"https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582\"><img src=https://img.shields.io/badge/-failure-red></a>\r\n|torch|<a href=\"https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582\"><img src=https://img.shields.io/badge/-failure-red></a>\r\n|numpy|<a href=\"https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582\"><img src=https://img.shields.io/badge/-failure-red></a>\r\n|jax|<a href=\"https://github.com/unifyai/ivy/actions/runs/6413197943/job/17411744582\"><img src=https://img.shields.io/badge/-failure-red></a>\r\n\n", "before_files": [{"content": "import ivy\nfrom ivy.functional.frontends.numpy.func_wrapper import (\n    to_ivy_arrays_and_back,\n    outputs_to_numpy_arrays,\n)\n\n\n@to_ivy_arrays_and_back\ndef indices(dimensions, dtype=int, sparse=False):\n    dimensions = tuple(dimensions)\n    N = len(dimensions)\n    shape = (1,) * N\n    if sparse:\n        res = tuple()\n    else:\n        res = ivy.empty((N,) + dimensions, dtype=dtype)\n    for i, dim in enumerate(dimensions):\n        idx = ivy.arange(dim, dtype=dtype).reshape(shape[:i] + (dim,) + shape[i + 1 :])\n        if sparse:\n            res = res + (idx,)\n        else:\n            res[i] = idx\n    return res\n\n\n# unravel_index\n@to_ivy_arrays_and_back\ndef unravel_index(indices, shape, order=\"C\"):\n    ret = [x.astype(\"int64\") for x in ivy.unravel_index(indices, shape)]\n    return tuple(ret)\n\n\n@outputs_to_numpy_arrays\ndef diag_indices(n, ndim=2):\n    idx = ivy.arange(n, dtype=int)\n    return (idx,) * ndim\n\n\n@to_ivy_arrays_and_back\ndef tril_indices(n, k=0, m=None):\n    return ivy.tril_indices(n, m, k)\n", "path": "ivy/functional/frontends/numpy/indexing_routines/generating_index_arrays.py"}]}