@harsha_g
I changed class like this to pass image into a transform function.
class DatasetProcessing(Dataset):
#initialise the class variables - transform, data, target
def __init__(self, data, target, transform=None): 
     #self.transform = transform 
    print("Before transformation")
    print(data.size())

    print(data[0].size())

    outputs = []
    datalen = data.size()[0]
    for i in range(datalen):          
        tensor = transform(data[i,:,:,:])   #transform 
        outputs.append(tensor)
    result = torch.cat(outputs, dim=1)  #shape (64, 32*in_channels, 224, 224)        

    print("After transformation")
    print(result.size())
    data = result
    self.data =  data.view(1, -1)
    # converting target to torch.LongTensor dtype
    self.target = target 

#retrieve the X and y index value and return it
def __getitem__(self, index):        
    return (self.data[index], self.target[index])
    
     return (self.transform(self.data[index]), self.target[index])

#returns the length of the data
def __len__(self): 
    return len(list(self.data))


OUTPUT from prints.

Before transformation
torch.Size([1316, 224, 224, 3])
torch.Size([224, 224, 3])
After transformation
torch.Size([3, 294784, 3])
Errror Message:
RuntimeError: size mismatch, m1: [3 x 221760], m2: [150528 x 1536] at /opt/conda/conda-bld/pytorch_1587428190859/work/aten/src/TH/generic/THTensorMath.cpp:41
Output from validation output: (Actually the output has to be showing 32 batches with 32 labels but am not sure why is it throwing belwo output
**IMAGES
torch.Size([3, 73920, 3])
**LABEL
tensor([0., 0., 0.])