content: # Torchvision compatibility fix for functional_tensor module
  # This file helps resolve compatibility issues between different torchvision versions
  
  import sys
  import torchvision
  
  def fix_torchvision_functional_tensor():
      """
      Fix torchvision.transforms.functional_tensor import issue
      """
      try:
          # Check if the module exists in the expected location
          import torchvision.transforms.functional_tensor
          print("torchvision.transforms.functional_tensor is available")
          return True
      except ImportError:
          print("torchvision.transforms.functional_tensor not found, applying compatibility fix...")
          
          try:
              # Create a mock functional_tensor module with the required functions
              import torch
              import torchvision.transforms.functional as F
              
              class FunctionalTensorMock:
                  """Mock module to replace functional_tensor"""
                  
                  @staticmethod
                  def rgb_to_grayscale(img, num_output_channels=1):
                      """Convert RGB image to grayscale"""
                      if hasattr(F, 'rgb_to_grayscale'):
                          return F.rgb_to_grayscale(img, num_output_channels)
                      else:
                          # Fallback implementation
                          if len(img.shape) == 4:  # Batch of images
                              weights = torch.tensor([0.299, 0.587, 0.114], 
                                                     device=img.device, dtype=img.dtype).view(1, 3, 1, 1)
                          else:  # Single image
                              weights = torch.tensor([0.299, 0.587, 0.114], 
                                                     device=img.device, dtype=img.dtype).view(3, 1, 1)
                          
                          grayscale = torch.sum(img * weights, dim=-3, keepdim=True)
                          
                          if num_output_channels == 3:
                              if len(img.shape) == 4:
                                  grayscale = grayscale.repeat(1, 3, 1, 1) 
                              else:
                                  grayscale = grayscale.repeat(3, 1, 1)
                          
                          return grayscale
                  
                  @staticmethod
                  def resize(img, size, interpolation=2, antialias=None):
                      """Resize function wrapper"""
                      try:
                          from torchvision.transforms.v2.functional import resize as v2_resize
                          return v2_resize(img, size, 
                                           interpolation=interpolation, 
                                           antialias=antialias)
                      except ImportError:
                          if hasattr(F, 'resize'):
                              return F.resize(img, size, interpolation=interpolation)
                          else:
                              import torch.nn.functional as torch_F
                              if isinstance(size, int):
                                  size = (size, size)
                              return torch_F.interpolate(
                                  img.unsqueeze(0) if len(img.shape) == 3 else img,
                                  size=size, mode='bilinear', align_corners=False)
                  
                  def __getattr__(self, name):
                      """Fallback to regular functional module"""
                      if hasattr(F, name):
                          return getattr(F, name)
                      else:
                          # Try v2.functional
                          try:
                              import torchvision.transforms.v2.functional as v2_F
                              if hasattr(v2_F, name):
                                  return getattr(v2_F, name)
                          except ImportError:
                              pass
                          
                          raise AttributeError(f"'{name}' not found in functional_tensor mock")
              
              # Create the mock module instance
              functional_tensor_mock = FunctionalTensorMock()
              
              # Monkey patch the old location
              sys.modules['torchvision.transforms.functional_tensor'] = functional_tensor_mock
              print("Applied compatibility fix: created functional_tensor mock module")
              return True
              
          except Exception as e:
              print(f"Failed to create functional_tensor mock: {e}")
              return False
  
  def apply_fix():
      """Apply the torchvision compatibility fix"""
      print(f"Torchvision version: {torchvision.__version__}")
      return fix_torchvision_functional_tensor()
  
  if __name__ == "__main__":
      apply_fix() 
      