print(infer.query(['G'],evidence={'D':0,'I':1})['G'])

The code you provided is using the `query` method of a `BayesianModel` object to perform probabilistic inference on a Bayesian network. Specifically, it is computing the posterior distribution over the variable 'G' given evidence that 'D'=0 and 'I'=1.

The `query` method returns a `DiscreteFactor` object representing the posterior distribution. The code then uses square brackets to try to access the probabilities for the values of 'G'. However, as I mentioned earlier, `DiscreteFactor` objects do not support indexing with square brackets.

To print the entire table of probabilities for the posterior distribution over 'G', you can use the `values` property of the `DiscreteFactor` object. Here's how you can modify the code to print the probabilities:

result = infer.query(['G'], evidence={'D': 0, 'I': 1})['G']
print(result.values)

This will print a list of probabilities for each possible value of 'G'. If you want to print the probabilities in a more readable format, you could use a loop:

result = infer.query(['G'], evidence={'D': 0, 'I': 1})['G']
for i, prob in enumerate(result.values):
    print(f'P(G={i}) = {prob:.3f}')

This will print each probability with two decimal places and a label indicating the value of 'G'.