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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 pdf(self, x_test):
 		""" 			Computes the probability density function at all x_test 		"""

		N,D = self.data.shape
		x_test = np.asfortranarray(x_test)
		x_test = x_test.reshape([-1, D])
		
		pdfs = self._individual_pdfs(x_test)
		#import pdb; pdb.set_trace()
		# combine values based on fully_dimensional!
		if self.fully_dimensional:
			# first the product of the individual pdfs for each point in the data across dimensions and then the average (factorized kernel)
			pdfs = np.sum(np.prod(pdfs, axis=-1)*self.weights[None, :], axis=-1)
		else:
			# first the average over the 1d pdfs and the the product over dimensions (TPE like factorization of the pdf)
			pdfs = np.prod(np.sum(pdfs*self.weights[None,:,None], axis=-2), axis=-1)
		return(pdfs)