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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def forward(self, input):
 """Evaluate forward pass on the input. Parameters input : `torch.tensor._TensorBase` Point at which to evaluate the operator. Returns ------- result : `torch.autograd.variable.Variable` Variable holding the result of the evaluation. Examples -------- Perform a matrix multiplication: Variable containing: 4 5 [torch.FloatTensor of size 2] Evaluate a functional, i.e., an operator with scalar output: Variable containing: 14 [torch.FloatTensor of size 1] """

        # TODO: batched evaluation
        if not self.operator.is_linear:
            # Only needed for nonlinear operators
            self.save_for_backward(input)

        # TODO: use GPU memory directly if possible
        input_arr = input.cpu().detach().numpy()
        if any(s == 0 for s in input_arr.strides):
            # TODO: remove when Numpy issue #9165 is fixed
            # https://github.com/numpy/numpy/pull/9177
            input_arr = input_arr.copy()

        op_result = self.operator(input_arr)
        if np.isscalar(op_result):
            # For functionals, the result is funnelled through `float`,
            # so we wrap it into a Numpy array with the same dtype as
            # `operator.domain`
            op_result = np.array(op_result, ndmin=1,
                                 dtype=self.operator.domain.dtype)
        tensor = torch.from_numpy(np.array(op_result, copy=False, ndmin=1))
        tensor = tensor.to(input.device)
        return tensor