How to use the lab.B.cast function in lab

def logpdf(self, x):
        """Compute the log-pdf.

        Args:
            x (input): Values to compute the log-pdf of.
            
        Returns:
            list[tensor]: Log-pdf for every input in `x`. If it can be
                determined that the list contains only a single log-pdf,
                then the list is flattened to a scalar.
        """
        logpdfs = -(B.logdet(self.var) +
                    B.cast(self.dtype, self.dim) *
                    B.cast(self.dtype, B.log_2_pi) +
                    B.iqf_diag(self.var, uprank(x) - self.mean)) / 2
        return logpdfs[0] if B.shape(logpdfs) == (1,) else logpdfs

def entropy(self):
        """Compute the entropy.
        
        Returns:
            scalar: The entropy.
        """
        return (B.logdet(self.var) +
                B.cast(self.dtype, self.dim) *
                B.cast(self.dtype, B.log_2_pi + 1)) / 2

def _compute(self, dists2):
        dtype = B.dtype(dists2)
        return B.cast(dtype, B.lt(dists2, B.cast(dtype, self.epsilon)))

def from_(cls, constant, ref):
        return cls(B.cast(B.dtype(ref), constant), *B.shape(ref))

def __init__(self, dtype, rows, cols=None):
        Constant.__init__(self, B.cast(dtype, 1), rows=rows, cols=cols)

def kl(self, other):
        """Compute the KL divergence with respect to another normal
        distribution.

        Args:
            other (:class:`.random.Normal`): Other normal.

        Returns:
            scalar: KL divergence.
        """
        return (B.ratio(self.var, other.var) +
                B.iqf_diag(other.var, other.mean - self.mean)[0] -
                B.cast(self.dtype, self.dim) +
                B.logdet(other.var) - B.logdet(self.var)) / 2

def __neg__(self):
        return mul(B.cast(B.dtype(self), -1), self)

def __init__(self, dtype, rows, cols=None):
        Constant.__init__(self, B.cast(dtype, 0), rows=rows, cols=cols)

def logpdf(self, x):
        """Compute the log-pdf.

        Args:
            x (input): Values to compute the log-pdf of.
            
        Returns:
            list[tensor]: Log-pdf for every input in `x`. If it can be
                determined that the list contains only a single log-pdf,
                then the list is flattened to a scalar.
        """
        logpdfs = -(B.logdet(self.var) +
                    B.cast(self.dtype, self.dim) *
                    B.cast(self.dtype, B.log_2_pi) +
                    B.iqf_diag(self.var, uprank(x) - self.mean)) / 2
        return logpdfs[0] if B.shape(logpdfs) == (1,) else logpdfs