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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 decode_cf_variable(name, var, concat_characters=True, mask_and_scale=True, decode_times=True, decode_endianness=True, stack_char_dim=True, use_cftime=None):
 """ Decodes a variable which may hold CF encoded information. This includes variables that have been masked and scaled, which hold CF style time variables (this is almost always the case if the dataset has been serialized) and which have strings encoded as character arrays. Parameters name: str Name of the variable. Used for better error messages. var : Variable A variable holding potentially CF encoded information. concat_characters : bool Should character arrays be concatenated to strings, for example: ['h', 'e', 'l', 'l', 'o'] -> 'hello' mask_and_scale: bool Lazily scale (using scale_factor and add_offset) and mask (using _FillValue). If the _Unsigned attribute is present treat integer arrays as unsigned. decode_times : bool Decode cf times ('hours since 2000-01-01') to np.datetime64. decode_endianness : bool Decode arrays from non-native to native endianness. stack_char_dim : bool Whether to stack characters into bytes along the last dimension of this array. Passed as an argument because we need to look at the full dataset to figure out if this is appropriate. use_cftime: bool, optional Only relevant if encoded dates come from a standard calendar (e.g. 'gregorian', 'proleptic_gregorian', 'standard', or not specified). If None (default), attempt to decode times to ``np.datetime64[ns]`` objects; if this is not possible, decode times to ``cftime.datetime`` objects. If True, always decode times to ``cftime.datetime`` objects, regardless of whether or not they can be represented using ``np.datetime64[ns]`` objects. If False, always decode times to ``np.datetime64[ns]`` objects; if this is not possible raise an error. Returns ------- out : Variable A variable holding the decoded equivalent of var. """

    var = as_variable(var)
    original_dtype = var.dtype

    if concat_characters:
        if stack_char_dim:
            var = strings.CharacterArrayCoder().decode(var, name=name)
        var = strings.EncodedStringCoder().decode(var)

    if mask_and_scale:
        for coder in [variables.UnsignedIntegerCoder(),
                      variables.CFMaskCoder(),
                      variables.CFScaleOffsetCoder()]:
            var = coder.decode(var, name=name)

    if decode_times:
        for coder in [times.CFTimedeltaCoder(),
                      times.CFDatetimeCoder(use_cftime=use_cftime)]:
            var = coder.decode(var, name=name)

    dimensions, data, attributes, encoding = (
        variables.unpack_for_decoding(var))
    # TODO(shoyer): convert everything below to use coders

    if decode_endianness and not data.dtype.isnative:
        # do this last, so it's only done if we didn't already unmask/scale
        data = NativeEndiannessArray(data)
        original_dtype = data.dtype

    encoding.setdefault('dtype', original_dtype)

    if 'dtype' in attributes and attributes['dtype'] == 'bool':
        del attributes['dtype']
        data = BoolTypeArray(data)

    if not isinstance(data, dask_array_type):
        data = indexing.LazilyOuterIndexedArray(data)

    return Variable(dimensions, data, attributes, encoding=encoding)