tf.division() - On dividing two tensors / a tensor and a integer
Produces a tensor of datatype float64 as default.
Doesn’t int32 / int32 results with a int32 value ?
Should I use tf.cast( , tf.int32) always to get this done ?
Sadly < 2 > is right acc. to tensorFlows library,
Which is against a general equation in mathematics.
x   *   (y/y) =  x      [where both x and y variables are of type int in LHS]
In terms of tensorFlow
tensor = tf.multiply(tf.divide(x, y), y)      #x - tensor(DT - int32), y - tensor or a variable (DT- int32)
print(tensor)
Should I stop overthinking over this and just stick with this rules ??
a.)  tf.division(tensor of DT int, tensor or variable of DT int)  = tensor of value < tensor/tensor or variable > of DT float64 always
b.) The same rule < a >  while dividing a tensor with a variable or another tensor of DT int  gives a tensor < tensor/tensor or variable > of DT float64 always.
If any agree this has to be addressed and I was right do support this to let this addressed by TensorFlow team. & to help me move on to further studies. !
ThankYou Dev_Friends !!