prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  [BUG] Pytorch dataloader because of transforms and shuffle=false
  ## 🐛🐛 Bug Report
  
  
  ### ⚗️ Current Behavior
  A clear and concise description of the behavior.
  
  ```python
  import hub
  ds = hub.load("hub://activeloop/mnist-test")
  
  dataloader = ds.pytorch(batch_size=2, num_workers=2, shuffle=False, transform={"images": None, "labels": None})
  for (images, labels) in dataloader:
    print(images.shape, labels.shape)
    break
  ```
  
  ```
  Opening dataset in read-only mode as you don't have write permissions.
  hub://activeloop/mnist-test loaded successfully.
  This dataset can be visualized at https://app.activeloop.ai/activeloop/mnist-test.
  ---------------------------------------------------------------------------
  AttributeError                            Traceback (most recent call last)
  [<ipython-input-21-22b652d8dbed>](https://localhost:8080/#) in <module>()
        4 dataloader = ds.pytorch(batch_size=2, num_workers=2, shuffle=False, transform={"images": None, "labels": None})
        5 for (images, labels) in dataloader:
  ----> 6   print(images.shape, labels.shape)
        7   break
  
  AttributeError: 'str' object has no attribute 'shape'
  ```
  
  but when you remove the argument `transform` from dataloader that script works.
  
  ### ⚙️ Environment
  
  - Google colab
  
  
  </issue>
  <code>
  [start of hub/integrations/pytorch/common.py]
  1 from typing import Callable, Dict, List, Optional
  2 from hub.util.iterable_ordered_dict import IterableOrderedDict
  3 import numpy as np
  4 
  5 
  6 def collate_fn(batch):
  7     import torch
  8 
  9     elem = batch[0]
  10 
  11     if isinstance(elem, IterableOrderedDict):
  12         return IterableOrderedDict(
  13             (key, collate_fn([d[key] for d in batch])) for key in elem.keys()
  14         )
  15 
  16     if isinstance(elem, np.ndarray) and elem.size > 0 and isinstance(elem[0], str):
  17         batch = [it[0] for it in batch]
  18     return torch.utils.data._utils.collate.default_collate(batch)
  19 
  20 
  21 def convert_fn(data):
  22     import torch
  23 
  24     if isinstance(data, IterableOrderedDict):
  25         return IterableOrderedDict((k, convert_fn(v)) for k, v in data.items())
  26     if isinstance(data, np.ndarray) and data.size > 0 and isinstance(data[0], str):
  27         data = data[0]
  28 
  29     return torch.utils.data._utils.collate.default_convert(data)
  30 
  31 
  32 class PytorchTransformFunction:
  33     def __init__(
  34         self,
  35         transform_dict: Optional[Dict[str, Optional[Callable]]] = None,
  36         composite_transform: Optional[Callable] = None,
  37         tensors: List[str] = None,
  38     ) -> None:
  39         self.composite_transform = composite_transform
  40         self.transform_dict = transform_dict
  41         tensors = tensors or []
  42 
  43         if transform_dict is not None:
  44             for tensor in transform_dict:
  45                 if tensor not in tensors:
  46                     raise ValueError(f"Invalid transform. Tensor {tensor} not found.")
  47 
  48     def __call__(self, data_in: Dict) -> Dict:
  49         if self.composite_transform is not None:
  50             return self.composite_transform(data_in)
  51         elif self.transform_dict is not None:
  52             data_out = {}
  53             for tensor, fn in self.transform_dict.items():
  54                 value = data_in[tensor]
  55                 data_out[tensor] = value if fn is None else fn(value)
  56             return data_out
  57         return data_in
  58
  [end of hub/integrations/pytorch/common.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/hub/integrations/pytorch/common.py b/hub/integrations/pytorch/common.py
  --- a/hub/integrations/pytorch/common.py
  +++ b/hub/integrations/pytorch/common.py
  @@ -53,5 +53,6 @@
               for tensor, fn in self.transform_dict.items():
                   value = data_in[tensor]
                   data_out[tensor] = value if fn is None else fn(value)
  +            data_out = IterableOrderedDict(data_out)
               return data_out
           return data_in
  
verification_info: {"golden_diff": "diff --git a/hub/integrations/pytorch/common.py b/hub/integrations/pytorch/common.py\n--- a/hub/integrations/pytorch/common.py\n+++ b/hub/integrations/pytorch/common.py\n@@ -53,5 +53,6 @@\n             for tensor, fn in self.transform_dict.items():\n                 value = data_in[tensor]\n                 data_out[tensor] = value if fn is None else fn(value)\n+            data_out = IterableOrderedDict(data_out)\n             return data_out\n         return data_in\n", "issue": "[BUG] Pytorch dataloader because of transforms and shuffle=false\n## \ud83d\udc1b\ud83d\udc1b Bug Report\r\n\r\n\r\n### \u2697\ufe0f Current Behavior\r\nA clear and concise description of the behavior.\r\n\r\n```python\r\nimport hub\r\nds = hub.load(\"hub://activeloop/mnist-test\")\r\n\r\ndataloader = ds.pytorch(batch_size=2, num_workers=2, shuffle=False, transform={\"images\": None, \"labels\": None})\r\nfor (images, labels) in dataloader:\r\n  print(images.shape, labels.shape)\r\n  break\r\n```\r\n\r\n```\r\nOpening dataset in read-only mode as you don't have write permissions.\r\nhub://activeloop/mnist-test loaded successfully.\r\nThis dataset can be visualized at https://app.activeloop.ai/activeloop/mnist-test.\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n[<ipython-input-21-22b652d8dbed>](https://localhost:8080/#) in <module>()\r\n      4 dataloader = ds.pytorch(batch_size=2, num_workers=2, shuffle=False, transform={\"images\": None, \"labels\": None})\r\n      5 for (images, labels) in dataloader:\r\n----> 6   print(images.shape, labels.shape)\r\n      7   break\r\n\r\nAttributeError: 'str' object has no attribute 'shape'\r\n```\r\n\r\nbut when you remove the argument `transform` from dataloader that script works.\r\n\r\n### \u2699\ufe0f Environment\r\n\r\n- Google colab\r\n\n", "before_files": [{"content": "from typing import Callable, Dict, List, Optional\nfrom hub.util.iterable_ordered_dict import IterableOrderedDict\nimport numpy as np\n\n\ndef collate_fn(batch):\n    import torch\n\n    elem = batch[0]\n\n    if isinstance(elem, IterableOrderedDict):\n        return IterableOrderedDict(\n            (key, collate_fn([d[key] for d in batch])) for key in elem.keys()\n        )\n\n    if isinstance(elem, np.ndarray) and elem.size > 0 and isinstance(elem[0], str):\n        batch = [it[0] for it in batch]\n    return torch.utils.data._utils.collate.default_collate(batch)\n\n\ndef convert_fn(data):\n    import torch\n\n    if isinstance(data, IterableOrderedDict):\n        return IterableOrderedDict((k, convert_fn(v)) for k, v in data.items())\n    if isinstance(data, np.ndarray) and data.size > 0 and isinstance(data[0], str):\n        data = data[0]\n\n    return torch.utils.data._utils.collate.default_convert(data)\n\n\nclass PytorchTransformFunction:\n    def __init__(\n        self,\n        transform_dict: Optional[Dict[str, Optional[Callable]]] = None,\n        composite_transform: Optional[Callable] = None,\n        tensors: List[str] = None,\n    ) -> None:\n        self.composite_transform = composite_transform\n        self.transform_dict = transform_dict\n        tensors = tensors or []\n\n        if transform_dict is not None:\n            for tensor in transform_dict:\n                if tensor not in tensors:\n                    raise ValueError(f\"Invalid transform. Tensor {tensor} not found.\")\n\n    def __call__(self, data_in: Dict) -> Dict:\n        if self.composite_transform is not None:\n            return self.composite_transform(data_in)\n        elif self.transform_dict is not None:\n            data_out = {}\n            for tensor, fn in self.transform_dict.items():\n                value = data_in[tensor]\n                data_out[tensor] = value if fn is None else fn(value)\n            return data_out\n        return data_in\n", "path": "hub/integrations/pytorch/common.py"}]}