def log_prob(self, response, predicted_linear_response, name=None):
with self._name_scope(<EOL>name, '<STR_LIT>', [response, predicted_linear_response]):<EOL><INDENT>dtype = dtype_util.common_dtype([response, predicted_linear_response])<EOL>response = tf.convert_to_tensor(<EOL>value=response, dtype=dtype, name='<STR_LIT>')<EOL>predicted_linear_response = tf.convert_to_tensor(<EOL>value=predicted_linear_response, name='<STR_LIT>')<EOL>return self._log_prob(response, predicted_linear_response)<EOL><DEDENT>
Computes `D(param=mean(r)).log_prob(response)` for linear response, `r`.

        Args:
          response: `float`-like `Tensor` representing observed ("actual")
            responses.
          predicted_linear_response: `float`-like `Tensor` corresponding to
            `tf.matmul(model_matrix, weights)`.
          name: Python `str` used as TF namescope for ops created by member
            functions. Default value: `None` (i.e., 'log_prob').

        Returns:
          log_prob: `Tensor` with shape and dtype of `predicted_linear_response`
            representing the distribution prescribed log-probability of the observed
            `response`s.