prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  broadcast_to
  
  
  </issue>
  <code>
  [start of ivy/functional/frontends/paddle/tensor/manipulation.py]
  1 # global
  2 import ivy
  3 from ivy.functional.frontends.paddle.func_wrapper import (
  4     to_ivy_arrays_and_back,
  5 )
  6 from ivy.func_wrapper import (
  7     with_unsupported_dtypes,
  8     with_supported_dtypes,
  9 )
  10 
  11 
  12 @to_ivy_arrays_and_back
  13 def reshape(x, shape):
  14     return ivy.reshape(x, shape)
  15 
  16 
  17 @with_unsupported_dtypes({"2.5.0 and below": ("float16", "bfloat16")}, "paddle")
  18 @to_ivy_arrays_and_back
  19 def abs(x, name=None):
  20     return ivy.abs(x)
  21 
  22 
  23 absolute = abs
  24 
  25 
  26 @to_ivy_arrays_and_back
  27 def stack(x, axis=0, name=None):
  28     return ivy.stack(x, axis=axis)
  29 
  30 
  31 @with_unsupported_dtypes({"2.5.0 and below": ("int8", "int16")}, "paddle")
  32 @to_ivy_arrays_and_back
  33 def concat(x, axis, name=None):
  34     return ivy.concat(x, axis=axis)
  35 
  36 
  37 @with_unsupported_dtypes(
  38     {"2.5.0 and below": ("int8", "uint8", "int16", "float16")},
  39     "paddle",
  40 )
  41 @to_ivy_arrays_and_back
  42 def tile(x, repeat_times, name=None):
  43     return ivy.tile(x, repeats=repeat_times)
  44 
  45 
  46 @with_unsupported_dtypes(
  47     {"2.5.0 and below": ("int16", "complex64", "complex128")},
  48     "paddle",
  49 )
  50 @to_ivy_arrays_and_back
  51 def split(x, num_or_sections, axis=0, name=None):
  52     return ivy.split(x, num_or_size_splits=num_or_sections, axis=axis)
  53 
  54 
  55 @with_unsupported_dtypes(
  56     {"2.5.0 and below": ("float16", "bfloat16", "int8", "int16")},
  57     "paddle",
  58 )
  59 @to_ivy_arrays_and_back
  60 def squeeze(x, axis=None, name=None):
  61     return ivy.squeeze(x, axis=axis)
  62 
  63 
  64 @with_supported_dtypes(
  65     {
  66         "2.5.0 and below": (
  67             "bool",
  68             "float16",
  69             "float32",
  70             "float64",
  71             "int32",
  72             "int64",
  73             "uint8",
  74         )
  75     },
  76     "paddle",
  77 )
  78 @to_ivy_arrays_and_back
  79 def cast(x, dtype):
  80     return ivy.astype(x, dtype)
  81
  [end of ivy/functional/frontends/paddle/tensor/manipulation.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/manipulation.py b/ivy/functional/frontends/paddle/tensor/manipulation.py
  --- a/ivy/functional/frontends/paddle/tensor/manipulation.py
  +++ b/ivy/functional/frontends/paddle/tensor/manipulation.py
  @@ -78,3 +78,12 @@
   @to_ivy_arrays_and_back
   def cast(x, dtype):
       return ivy.astype(x, dtype)
  +
  +
  +@with_supported_dtypes(
  +    {"2.5.0 and below": ("bool", "float32", "float64", "int32", "int64")},
  +    "paddle",
  +)
  +@to_ivy_arrays_and_back
  +def broadcast_to(x, shape, name=None):
  +    return ivy.broadcast_to(x, shape)
  
verification_info: {"golden_diff": "diff --git a/ivy/functional/frontends/paddle/tensor/manipulation.py b/ivy/functional/frontends/paddle/tensor/manipulation.py\n--- a/ivy/functional/frontends/paddle/tensor/manipulation.py\n+++ b/ivy/functional/frontends/paddle/tensor/manipulation.py\n@@ -78,3 +78,12 @@\n @to_ivy_arrays_and_back\n def cast(x, dtype):\n     return ivy.astype(x, dtype)\n+\n+\n+@with_supported_dtypes(\n+    {\"2.5.0 and below\": (\"bool\", \"float32\", \"float64\", \"int32\", \"int64\")},\n+    \"paddle\",\n+)\n+@to_ivy_arrays_and_back\n+def broadcast_to(x, shape, name=None):\n+    return ivy.broadcast_to(x, shape)\n", "issue": "broadcast_to\n\n", "before_files": [{"content": "# global\nimport ivy\nfrom ivy.functional.frontends.paddle.func_wrapper import (\n    to_ivy_arrays_and_back,\n)\nfrom ivy.func_wrapper import (\n    with_unsupported_dtypes,\n    with_supported_dtypes,\n)\n\n\n@to_ivy_arrays_and_back\ndef reshape(x, shape):\n    return ivy.reshape(x, shape)\n\n\n@with_unsupported_dtypes({\"2.5.0 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef abs(x, name=None):\n    return ivy.abs(x)\n\n\nabsolute = abs\n\n\n@to_ivy_arrays_and_back\ndef stack(x, axis=0, name=None):\n    return ivy.stack(x, axis=axis)\n\n\n@with_unsupported_dtypes({\"2.5.0 and below\": (\"int8\", \"int16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef concat(x, axis, name=None):\n    return ivy.concat(x, axis=axis)\n\n\n@with_unsupported_dtypes(\n    {\"2.5.0 and below\": (\"int8\", \"uint8\", \"int16\", \"float16\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef tile(x, repeat_times, name=None):\n    return ivy.tile(x, repeats=repeat_times)\n\n\n@with_unsupported_dtypes(\n    {\"2.5.0 and below\": (\"int16\", \"complex64\", \"complex128\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef split(x, num_or_sections, axis=0, name=None):\n    return ivy.split(x, num_or_size_splits=num_or_sections, axis=axis)\n\n\n@with_unsupported_dtypes(\n    {\"2.5.0 and below\": (\"float16\", \"bfloat16\", \"int8\", \"int16\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef squeeze(x, axis=None, name=None):\n    return ivy.squeeze(x, axis=axis)\n\n\n@with_supported_dtypes(\n    {\n        \"2.5.0 and below\": (\n            \"bool\",\n            \"float16\",\n            \"float32\",\n            \"float64\",\n            \"int32\",\n            \"int64\",\n            \"uint8\",\n        )\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef cast(x, dtype):\n    return ivy.astype(x, dtype)\n", "path": "ivy/functional/frontends/paddle/tensor/manipulation.py"}]}