tensorflow_probability/python/distributions/dirichlet_multinomial.py
def _maybe_assert_valid_sample(self, counts):
    """Check counts for proper shape, values, then return tensor version."""
    if not self.validate_args:
      return counts
    counts = distribution_util.embed_check_nonnegative_integer_form(counts)
    return distribution_util.with_dependencies([
        assert_util.assert_equal(
            self.total_count,
            tf.reduce_sum(input_tensor=counts, axis=-1),
            message="counts last-dimension must sum to `self.total_count`"),
    ], counts)
def _maybe_assert_valid_sample(self, counts):
    """Check counts for proper shape, values, then return tensor version."""
    if not self.validate_args:
      return counts
    counts = distribution_util.embed_check_nonnegative_integer_form(counts)
    return distribution_util.with_dependencies([
        assert_util.assert_equal(
            self.total_count,
            tf.reduce_sum(input_tensor=counts, axis=-1),
            message="counts last-dimension must sum to `self.total_count`"),
    ], counts)
def _maybe_assert_valid_sample ( self , counts ) : if not self . validate_args : return counts counts = distribution_util . embed_check_nonnegative_integer_form ( counts ) return distribution_util . with_dependencies ( [ assert_util . assert_equal ( self . total_count , tf . reduce_sum ( input_tensor = counts , axis = - 1 ) , message = "counts last-dimension must sum to `self.total_count`" ) , ] , counts )
Check counts for proper shape, values, then return tensor version.
Check counts for proper shape values then return tensor version .