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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 ensure_type(val, dtype, ndim, name, length=None, can_be_none=False, shape=None, warn_on_cast=True, add_newaxis_on_deficient_ndim=False):
 """Typecheck the size, shape and dtype of a numpy array, with optional casting. Parameters val : {np.ndaraay, None} The array to check dtype : {nd.dtype, str} The dtype you'd like the array to have ndim : int The number of dimensions you'd like the array to have name : str name of the array. This is used when throwing exceptions, so that we can describe to the user which array is messed up. length : int, optional How long should the array be? can_be_none : bool Is ``val == None`` acceptable? shape : tuple, optional What should be shape of the array be? If the provided tuple has Nones in it, those will be semantically interpreted as matching any length in that dimension. So, for example, using the shape spec ``(None, None, 3)`` will ensure that the last dimension is of length three without constraining the first two dimensions warn_on_cast : bool, default=True Raise a warning when the dtypes don't match and a cast is done. add_newaxis_on_deficient_ndim : bool, default=True Add a new axis to the beginining of the array if the number of dimensions is deficient by one compared to your specification. For instance, if you're trying to get out an array of ``ndim == 3``, but the user provides an array of ``shape == (10, 10)``, a new axis will be created with length 1 in front, so that the return value is of shape ``(1, 10, 10)``. Notes ----- The returned value will always be C-contiguous. Returns ------- typechecked_val : np.ndarray, None If `val=None` and `can_be_none=True`, then this will return None. Otherwise, it will return val (or a copy of val). If the dtype wasn't right, it'll be casted to the right shape. If the array was not C-contiguous, it'll be copied as well. """

    if can_be_none and val is None:
        return None

    if not isinstance(val, np.ndarray):
        # special case: if the user is looking for a 1d array, and
        # they request newaxis upconversion, and provided a scalar
        # then we should reshape the scalar to be a 1d length-1 array
        if add_newaxis_on_deficient_ndim and ndim == 1 and np.isscalar(val):
            val = np.array([val])
        else:
            raise TypeError(("%s must be numpy array. "
                             " You supplied type %s" % (name, type(val))))

    if warn_on_cast and val.dtype != dtype:
        warnings.warn("Casting %s dtype=%s to %s " % (name, val.dtype, dtype),
                      TypeCastPerformanceWarning)

    if not val.ndim == ndim:
        if add_newaxis_on_deficient_ndim and val.ndim + 1 == ndim:
            val = val[np.newaxis, ...]
        else:
            raise ValueError(("%s must be ndim %s. "
                              "You supplied %s" % (name, ndim, val.ndim)))

    val = np.ascontiguousarray(val, dtype=dtype)

    if length is not None and len(val) != length:
        raise ValueError(("%s must be length %s. "
                          "You supplied %s" % (name, length, len(val))))

    if shape is not None:
        # the shape specified given by the user can look like (None, None 3)
        # which indicates that ANY length is accepted in dimension 0 or
        # dimension 1
        sentenel = object()
        error = ValueError(("%s must be shape %s. You supplied  "
                            "%s" % (name, str(shape).replace('None', 'Any'), val.shape)))
        for a, b in zip_longest(val.shape, shape, fillvalue=sentenel):
            if a is sentenel or b is sentenel:
                # if the sentenel was reached, it means that the ndim didn't
                # match or something. this really shouldn't happen
                raise error
            if b is None:
                # if the user's shape spec has a None in it, it matches anything
                continue
            if a != b:
                # check for equality
                raise error

    return val