Let’s go through your code.
img = inference_transform(img.convert("RGB")) 

So, you have loaded and transformed an image. Your image should have a shape of channel, height, width.
Now, let’s see here,
img = img.reshape((1,img.shape[0], img.shape[1], img.shape[2])) 

When you perform reshape operation img.reshape((1,img.shape[0], img.shape[1], img.shape[2])), the shape becomes 1, channel, height, width, where 1 at the beginning acts like the batch. Now, your image is a 4d tensor and has a shape of batch, channel, height, width.  Your image is now ready for your model, assuming it works with images with shape batch, channel, height, width.
However, here’s a problem,
img = img.unsqueeze(0)

When you perform img.unsqueeze(0), it creates another dimension at 0th index. Now, your image has become a 5d tensor with shape batch, number_of_frames, channel, height, width.
So, when you do this,
B, C, H, W = x.shape

You are trying to unpack 5 values into 4 placeholders (Remember, x has 5 values now!).
So, just remove the reshape line or unsqueeze line and you should be ok.
img = img.reshape((1,img.shape[0], img.shape[1], img.shape[2]))   #<------ this
img = img.unsqueeze(0) #<------ or this

I have a crush on unsqueeze operation (haha), so I would suggest commenting out the reshape line.
Cheers!