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#Concatenate the batch_id of the image to the detection#this helps us identify which image does the detection correspond to 
#We use a linear straucture to hold ALL the detections from the batch
#the batch_dim is flattened
#batch is identified by extra batch column
#creating a row with index of images

batch_ind = image_pred_class.new(image_pred_class.size(0), 1).fill_(ind)
seq = batch_ind, image_pred_class
if not write:
    output = torch.cat(seq,1)
    write = True
else:
    out = torch.cat(seq,1)
    output = torch.cat((output,out))