prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  meshgrid
  
  
  </issue>
  <code>
  [start of ivy/functional/frontends/paddle/tensor/creation.py]
  1 # global
  2 import ivy
  3 from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  4 from .tensor import Tensor
  5 from ivy.functional.frontends.paddle.func_wrapper import (
  6     to_ivy_arrays_and_back,
  7 )
  8 
  9 
  10 @to_ivy_arrays_and_back
  11 def to_tensor(data, /, *, dtype=None, place=None, stop_gradient=True):
  12     array = ivy.array(data, dtype=dtype, device=place)
  13     return Tensor(array, dtype=dtype, place=place)
  14 
  15 
  16 @with_unsupported_dtypes({"2.5.0 and below": "int8"}, "paddle")
  17 @to_ivy_arrays_and_back
  18 def ones(shape, /, *, dtype=None, name=None):
  19     dtype = "float32" if dtype is None else dtype
  20     return ivy.ones(shape, dtype=dtype)
  21 
  22 
  23 @with_unsupported_dtypes(
  24     {"2.5.0 and below": ("uint8", "int8", "complex64", "complex128")}, "paddle"
  25 )
  26 @to_ivy_arrays_and_back
  27 def ones_like(x, /, *, dtype=None, name=None):
  28     dtype = x.dtype if dtype is None else dtype
  29     return ivy.ones_like(x, dtype=dtype)
  30 
  31 
  32 @with_unsupported_dtypes({"2.5.0 and below": "int8"}, "paddle")
  33 @to_ivy_arrays_and_back
  34 def zeros(shape, /, *, dtype=None, name=None):
  35     dtype = "float32" if dtype is None else dtype
  36     return ivy.zeros(shape, dtype=dtype)
  37 
  38 
  39 @with_unsupported_dtypes(
  40     {"2.5.0 and below": ("uint8", "int8", "complex64", "complex128")}, "paddle"
  41 )
  42 @to_ivy_arrays_and_back
  43 def zeros_like(x, /, *, dtype=None, name=None):
  44     dtype = x.dtype if dtype is None else dtype
  45     return ivy.zeros_like(x, dtype=dtype)
  46 
  47 
  48 @to_ivy_arrays_and_back
  49 def full(shape, fill_value, /, *, dtype=None, name=None):
  50     dtype = "float32" if dtype is None else dtype
  51     return ivy.full(shape, fill_value, dtype=dtype)
  52 
  53 
  54 @to_ivy_arrays_and_back
  55 def full_like(x, fill_value, /, *, dtype=None, name=None):
  56     dtype = x.dtype if dtype is None else dtype
  57     return ivy.full_like(x, fill_value, dtype=dtype)
  58 
  59 
  60 @with_unsupported_dtypes({"2.5.0 and below": ("float16", "bfloat16")}, "paddle")
  61 @to_ivy_arrays_and_back
  62 def arange(start, end=None, step=1, dtype=None, name=None):
  63     return ivy.arange(start, end, step=step, dtype=dtype)
  64 
  65 
  66 @to_ivy_arrays_and_back
  67 def empty(shape, dtype=None):
  68     return ivy.empty(shape=shape, dtype=dtype)
  69 
  70 
  71 @to_ivy_arrays_and_back
  72 def eye(num_rows, num_columns=None, dtype=None, name=None):
  73     return ivy.eye(num_rows, num_columns, dtype=dtype)
  74 
  75 
  76 @to_ivy_arrays_and_back
  77 def empty_like(x, dtype=None, name=None):
  78     return ivy.empty_like(x, dtype=dtype)
  79 
  80 
  81 @with_unsupported_dtypes(
  82     {
  83         "2.5.0 and below": (
  84             "uint8",
  85             "int8",
  86             "int16",
  87             "float16",
  88             "complex64",
  89             "complex128",
  90             "bool",
  91         )
  92     },
  93     "paddle",
  94 )
  95 @to_ivy_arrays_and_back
  96 def tril(x, diagonal=0, name=None):
  97     return ivy.tril(x, k=diagonal)
  98 
  99 
  100 @with_unsupported_dtypes(
  101     {
  102         "2.5.0 and below": (
  103             "uint8",
  104             "int8",
  105             "int16",
  106             "float16",
  107             "complex64",
  108             "complex128",
  109             "bool",
  110         )
  111     },
  112     "paddle",
  113 )
  114 @to_ivy_arrays_and_back
  115 def triu(x, diagonal=0, name=None):
  116     return ivy.triu(x, k=diagonal)
  117 
  118 
  119 @with_supported_dtypes(
  120     {"2.5.0 and below": ("float32", "float64", "int32", "int64")}, "paddle"
  121 )
  122 @to_ivy_arrays_and_back
  123 def diagflat(x, offset=0, name=None):
  124     arr = ivy.diagflat(x, offset=offset)
  125     return arr
  126
  [end of ivy/functional/frontends/paddle/tensor/creation.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/paddle/tensor/creation.py b/ivy/functional/frontends/paddle/tensor/creation.py
  --- a/ivy/functional/frontends/paddle/tensor/creation.py
  +++ b/ivy/functional/frontends/paddle/tensor/creation.py
  @@ -123,3 +123,11 @@
   def diagflat(x, offset=0, name=None):
       arr = ivy.diagflat(x, offset=offset)
       return arr
  +
  +
  +@with_supported_dtypes(
  +    {"2.5.0 and below": ("float32", "float64", "int32", "int64")}, "paddle"
  +)
  +@to_ivy_arrays_and_back
  +def meshgrid(*args, **kwargs):
  +    return ivy.meshgrid(*args, indexing="ij")
  
verification_info: {"golden_diff": "diff --git a/ivy/functional/frontends/paddle/tensor/creation.py b/ivy/functional/frontends/paddle/tensor/creation.py\n--- a/ivy/functional/frontends/paddle/tensor/creation.py\n+++ b/ivy/functional/frontends/paddle/tensor/creation.py\n@@ -123,3 +123,11 @@\n def diagflat(x, offset=0, name=None):\r\n     arr = ivy.diagflat(x, offset=offset)\r\n     return arr\r\n+\r\n+\r\n+@with_supported_dtypes(\r\n+    {\"2.5.0 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\r\n+)\r\n+@to_ivy_arrays_and_back\r\n+def meshgrid(*args, **kwargs):\r\n+    return ivy.meshgrid(*args, indexing=\"ij\")\n", "issue": "meshgrid\n\n", "before_files": [{"content": "# global\r\nimport ivy\r\nfrom ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes\r\nfrom .tensor import Tensor\r\nfrom ivy.functional.frontends.paddle.func_wrapper import (\r\n    to_ivy_arrays_and_back,\r\n)\r\n\r\n\r\n@to_ivy_arrays_and_back\r\ndef to_tensor(data, /, *, dtype=None, place=None, stop_gradient=True):\r\n    array = ivy.array(data, dtype=dtype, device=place)\r\n    return Tensor(array, dtype=dtype, place=place)\r\n\r\n\r\n@with_unsupported_dtypes({\"2.5.0 and below\": \"int8\"}, \"paddle\")\r\n@to_ivy_arrays_and_back\r\ndef ones(shape, /, *, dtype=None, name=None):\r\n    dtype = \"float32\" if dtype is None else dtype\r\n    return ivy.ones(shape, dtype=dtype)\r\n\r\n\r\n@with_unsupported_dtypes(\r\n    {\"2.5.0 and below\": (\"uint8\", \"int8\", \"complex64\", \"complex128\")}, \"paddle\"\r\n)\r\n@to_ivy_arrays_and_back\r\ndef ones_like(x, /, *, dtype=None, name=None):\r\n    dtype = x.dtype if dtype is None else dtype\r\n    return ivy.ones_like(x, dtype=dtype)\r\n\r\n\r\n@with_unsupported_dtypes({\"2.5.0 and below\": \"int8\"}, \"paddle\")\r\n@to_ivy_arrays_and_back\r\ndef zeros(shape, /, *, dtype=None, name=None):\r\n    dtype = \"float32\" if dtype is None else dtype\r\n    return ivy.zeros(shape, dtype=dtype)\r\n\r\n\r\n@with_unsupported_dtypes(\r\n    {\"2.5.0 and below\": (\"uint8\", \"int8\", \"complex64\", \"complex128\")}, \"paddle\"\r\n)\r\n@to_ivy_arrays_and_back\r\ndef zeros_like(x, /, *, dtype=None, name=None):\r\n    dtype = x.dtype if dtype is None else dtype\r\n    return ivy.zeros_like(x, dtype=dtype)\r\n\r\n\r\n@to_ivy_arrays_and_back\r\ndef full(shape, fill_value, /, *, dtype=None, name=None):\r\n    dtype = \"float32\" if dtype is None else dtype\r\n    return ivy.full(shape, fill_value, dtype=dtype)\r\n\r\n\r\n@to_ivy_arrays_and_back\r\ndef full_like(x, fill_value, /, *, dtype=None, name=None):\r\n    dtype = x.dtype if dtype is None else dtype\r\n    return ivy.full_like(x, fill_value, dtype=dtype)\r\n\r\n\r\n@with_unsupported_dtypes({\"2.5.0 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\r\n@to_ivy_arrays_and_back\r\ndef arange(start, end=None, step=1, dtype=None, name=None):\r\n    return ivy.arange(start, end, step=step, dtype=dtype)\r\n\r\n\r\n@to_ivy_arrays_and_back\r\ndef empty(shape, dtype=None):\r\n    return ivy.empty(shape=shape, dtype=dtype)\r\n\r\n\r\n@to_ivy_arrays_and_back\r\ndef eye(num_rows, num_columns=None, dtype=None, name=None):\r\n    return ivy.eye(num_rows, num_columns, dtype=dtype)\r\n\r\n\r\n@to_ivy_arrays_and_back\r\ndef empty_like(x, dtype=None, name=None):\r\n    return ivy.empty_like(x, dtype=dtype)\r\n\r\n\r\n@with_unsupported_dtypes(\r\n    {\r\n        \"2.5.0 and below\": (\r\n            \"uint8\",\r\n            \"int8\",\r\n            \"int16\",\r\n            \"float16\",\r\n            \"complex64\",\r\n            \"complex128\",\r\n            \"bool\",\r\n        )\r\n    },\r\n    \"paddle\",\r\n)\r\n@to_ivy_arrays_and_back\r\ndef tril(x, diagonal=0, name=None):\r\n    return ivy.tril(x, k=diagonal)\r\n\r\n\r\n@with_unsupported_dtypes(\r\n    {\r\n        \"2.5.0 and below\": (\r\n            \"uint8\",\r\n            \"int8\",\r\n            \"int16\",\r\n            \"float16\",\r\n            \"complex64\",\r\n            \"complex128\",\r\n            \"bool\",\r\n        )\r\n    },\r\n    \"paddle\",\r\n)\r\n@to_ivy_arrays_and_back\r\ndef triu(x, diagonal=0, name=None):\r\n    return ivy.triu(x, k=diagonal)\r\n\r\n\r\n@with_supported_dtypes(\r\n    {\"2.5.0 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\r\n)\r\n@to_ivy_arrays_and_back\r\ndef diagflat(x, offset=0, name=None):\r\n    arr = ivy.diagflat(x, offset=offset)\r\n    return arr\r\n", "path": "ivy/functional/frontends/paddle/tensor/creation.py"}]}