Fix handling input_size with multi-input

[ahmedhshahin]

While dealing with multi-input, the current implementation calculates the number of elements in input as the product of all dimensions in all inputs, I believe this is not accurate.
For example, if we have input1 with dimensions [1,5,5] and input2 with dimensions [1,10,10]:
Current implementation: number of elements = 1 * 5 * 5 * 1 * 10 * 10 = 2500 elements
Where it should be: number of elements = (1 * 5 * 5) + (1 * 10 * 10) = 125 elements
As they are two separate inputs.

[scratchmex]

When the inputs are different length, for example: [(1, 28, 28), (1,)]; the nowadays implementation throws TypeError: can't multiply sequence by non-int of type 'tuple' and a warning

numpy\core\fromnumeric.py:87: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray

I think this PR solves this.

@sksq96

[scratchmex]

we don't need to check if input_size is a list because that is normalized here:

pytorch-summary/torchsummary/torchsummary.py

Lines 54 to 56 in 4852f2e

	# multiple inputs to the network
	if isinstance(input_size, tuple):
	input_size = [input_size]

[ahmedhshahin]

I wrote this a long time ago, but I think I added this check so that the code works properly for the single-input cases too. If its a single-input, return the product of input dims, if its multi-input (list of inputs), return prod(input1) + prod(input2) + ...