(self, x)
return x.view(x.size(0), -1)
Arguments:
            x: a float tensor with shape [batch_size, c, h, w].
        Returns:
            a float tensor with shape [batch_size, c*h*w].
Arguments:
            x: a float tensor with shape [batch_size, c, h, w].
        Returns:
            a float tensor with shape [batch_size, c*h*w].
Arguments : x : a float tensor with shape [ batch_size c h w ] . Returns : a float tensor with shape [ batch_size c * h * w ] .
def forward(self, x):
        """
        Arguments:
            x: a float tensor with shape [batch_size, c, h, w].
        Returns:
            a float tensor with shape [batch_size, c*h*w].
        """

        # without this pretrained model isn't working
        x = x.transpose(3, 2).contiguous()

        return x.view(x.size(0), -1)
def forward ( self , x ) : # without this pretrained model isn't working x = x . transpose ( 3 , 2 ) . contiguous ( ) return x . view ( x . size ( 0 ) , - 1 )