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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, x):
 """Compute forward-pass of this module on ``x``. Parameters x : `torch.autograd.variable.Variable` Input of this layer. The contained tensor must have shape ``extra_shape + operator.domain.shape``, and ``len(extra_shape)`` must be at least 1 (batch axis). Returns ------- out : `torch.autograd.variable.Variable` The computed output. Its tensor will have shape ``extra_shape + operator.range.shape``, where ``extra_shape`` are the extra axes of ``x``. Examples -------- Evaluating on a 2D tensor, where the operator expects a 1D input, i.e., with extra batch axis only: (3,) (2,) Variable containing: 1 2 [torch.FloatTensor of size 1x2] Variable containing: 0 0 1 2 [torch.FloatTensor of size 2x2] An arbitrary number of axes is supported: Variable containing: (0 ,.,.) = 1 2 [torch.FloatTensor of size 1x1x2] Variable containing: (0 ,.,.) = 0 0 1 2 <BLANKLINE> (1 ,.,.) = 2 4 3 6 <BLANKLINE> (2 ,.,.) = 4 8 5 10 [torch.FloatTensor of size 3x2x2] """

        in_shape = x.data.shape
        op_in_shape = self.op_func.operator.domain.shape
        op_out_shape = self.op_func.operator.range.shape

        extra_shape = in_shape[:-len(op_in_shape)]

        if in_shape[-len(op_in_shape):] != op_in_shape or not extra_shape:
            shp_str = str(op_in_shape).strip('()')
            raise ValueError('expected input of shape (N, *, {}), got input '
                             'with shape {}'.format(shp_str, in_shape))

        # Flatten extra axes, then do one entry at a time
        newshape = (int(np.prod(extra_shape)),) + op_in_shape
        x_flat_xtra = x.reshape(*newshape)
        results = []
        for i in range(x_flat_xtra.data.shape[0]):
            results.append(self.op_func(x_flat_xtra[i]))

        # Reshape the resulting stack to the expected output shape
        stack_flat_xtra = torch.stack(results)
        return stack_flat_xtra.view(extra_shape + op_out_shape)