tinynn/graph/modifier.py [1728:1778]:
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                tensor_changes[id_o] = [dim_choose_i]

                valid = True
                for m in self.pre_modifiers(self.pre_tensors()[0]):
                    valid = m.dim_choose_traversal(modifiers, tensor_changes, self.pre_tensors()[0])
                    if not valid:
                        break

                if valid > 0:
                    for m in self.next_modifiers(self.next_tensors()[0]):
                        valid = m.dim_choose_traversal(modifiers, tensor_changes, self.next_tensors()[0])
                        if not valid:
                            break

                if valid:
                    return True
                else:
                    if id_i in tensor_changes.keys():
                        del tensor_changes[id_i]
                    if id_o in tensor_changes.keys():
                        del tensor_changes[id_o]
            return False
        else:
            return True

    def change_dimension(self) -> bool:
        dim_changes_o = [self.dim_c]

        fill_tensor_by_dim_changes(self.output_tensor, dim_changes_o)

        tensor_constraint = self.dim_changes_info.update_o(
            self, self.next_tensors()[0], dim_changes_o, update_constraint=True
        )

        for m in self.next_modifiers():
            m.dim_change_forward(self, self.next_tensors()[0], dim_changes_o, None, tensor_constraint)

        return True

    def dim_change_forward(self, center, tensor, dim_changes_i, dim_transform, tensor_constraint):
        self.dim_changes_info.update_i(
            center, tensor, dim_changes_i, dim_transform, tensor_constraint=tensor_constraint
        )

        # Full connection can isolate changes in dim_c dimension
        dim_changes_o = deepcopy(dim_changes_i)
        if self.dim_c in dim_changes_o:
            dim_changes_o.remove(self.dim_c)

        # Dimension changes other than dim_c need to be passed to downstream nodes
        if len(dim_changes_o) > 0:
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tinynn/graph/modifier.py [2159:2207]:
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            tensor_changes[id_o] = [dim_choose_i]

            valid = True
            for m in self.pre_modifiers(self.pre_tensors()[0]):
                valid = m.dim_choose_traversal(modifiers, tensor_changes, self.pre_tensors()[0])
                if not valid:
                    break

            if valid > 0:
                for m in self.next_modifiers(self.next_tensors()[0]):
                    valid = m.dim_choose_traversal(modifiers, tensor_changes, self.next_tensors()[0])
                    if not valid:
                        break

            if valid:
                return True
            else:
                if id_i in tensor_changes.keys():
                    del tensor_changes[id_i]
                if id_o in tensor_changes.keys():
                    del tensor_changes[id_o]
            return False
        else:
            return True

    def change_dimension(self) -> bool:
        dim_changes_o = [self.dim_c]

        fill_tensor_by_dim_changes(self.output_tensor, dim_changes_o)

        tensor_constraint = self.dim_changes_info.update_o(
            self, self.next_tensors()[0], dim_changes_o, update_constraint=True
        )

        for m in self.next_modifiers():
            m.dim_change_forward(self, self.next_tensors()[0], dim_changes_o, None, tensor_constraint)

        return True

    def dim_change_forward(self, center, tensor, dim_changes_i, dim_transform, tensor_constraint):
        self.dim_changes_info.update_i(
            center, tensor, dim_changes_i, dim_transform, tensor_constraint=tensor_constraint
        )

        dim_changes_o = deepcopy(dim_changes_i)
        if self.dim_c in dim_changes_o:
            dim_changes_o.remove(self.dim_c)

        if len(dim_changes_o) > 0:
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