The error UNetMidBlock3D' object has no attribute 'downsample' suggests that the downsample attribute or method you're trying to access does not exist on the UNetMidBlock3D object in your code. This could be due to:

A typo in your code.
Using an older version of the library where the attribute doesn't exist.
Incorrect assumption that downsample is a valid attribute/method for the object.
Here’s how you can rewrite or debug this:

1. Check the Documentation or Code
   Verify if UNetMidBlock3D has a downsample attribute in the library you're using. If not, check for an alternative or related attribute/method.

2. Provide a Default Fallback
   Use hasattr to ensure that the attribute exists before accessing it:

python
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if hasattr(UNetMidBlock3D, 'downsample'):
downsample_layer = UNetMidBlock3D.downsample
else:
downsample_layer = None # or a suitable alternative
3. Explicitly Define the Downsample Logic
If the downsample attribute isn't provided, implement your own downsampling logic:

python
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import torch.nn as nn

class CustomUNetMidBlock3D(nn.Module):
def init(self, ...): # Add required parameters
super(CustomUNetMidBlock3D, self).init()
self.downsample = nn.MaxPool3d(kernel_size=2, stride=2) # Example downsampling

def forward(self, x):
    x = self.downsample(x)
    return x

4. Refactor Your Code
   Replace direct access to downsample with a custom implementation or avoid using it if unnecessary.