def transform_cell(self, raw_cell):
    'Transform an input cell before parsing it.\n\n        Static transformations, implemented in IPython.core.inputtransformer2,\n        deal with things like ``%magic`` and ``!system`` commands.\n        These run on all input.\n        Dynamic transformations, for things like unescaped magics and the exit\n        autocall, depend on the state of the interpreter.\n        These only apply to single line inputs.\n\n        These string-based transformations are followed by AST transformations;\n        see :meth:`transform_ast`.\n        '
    cell = self.input_transformer_manager.transform_cell(raw_cell)
    if (len(cell.splitlines()) == 1):
        with self.builtin_trap:
            cell = (self.prefilter_manager.prefilter_lines(cell) + '\n')
    lines = cell.splitlines(keepends=True)
    for transform in self.input_transformers_post:
        lines = transform(lines)
    cell = ''.join(lines)
    return cell
Transform an input cell before parsing it.

Static transformations, implemented in IPython.core.inputtransformer2,
deal with things like ``%magic`` and ``!system`` commands.
These run on all input.
Dynamic transformations, for things like unescaped magics and the exit
autocall, depend on the state of the interpreter.
These only apply to single line inputs.

These string-based transformations are followed by AST transformations;
see :meth:`transform_ast`.
def transform_cell(self, raw_cell):
    'Transform an input cell before parsing it.\n\n        Static transformations, implemented in IPython.core.inputtransformer2,\n        deal with things like ``%magic`` and ``!system`` commands.\n        These run on all input.\n        Dynamic transformations, for things like unescaped magics and the exit\n        autocall, depend on the state of the interpreter.\n        These only apply to single line inputs.\n\n        These string-based transformations are followed by AST transformations;\n        see :meth:`transform_ast`.\n        '
    cell = self.input_transformer_manager.transform_cell(raw_cell)
    if (len(cell.splitlines()) == 1):
        with self.builtin_trap:
            cell = (self.prefilter_manager.prefilter_lines(cell) + '\n')
    lines = cell.splitlines(keepends=True)
    for transform in self.input_transformers_post:
        lines = transform(lines)
    cell = .join(lines)
    return cell