TTypes< T, NDIMS >::ConstTensor tensorflow::Tensor::shaped(gtl::ArraySlice< int64 > new_sizes) const

tf.Session.__enter__()

tf.contrib.distributions.Gamma.std(name='std') Standard deviation.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.clone(name=None, **dist_args)

tf.contrib.learn.LinearRegressor.__repr__()

tf.contrib.distributions.Bernoulli.logits

tf.matrix_determinant(input, name=None) Computes the determinant of one ore more square matrices. The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. The output is a tensor containing the determinants for all input submatrices [..., :, :]. Args: input: A Tensor. Must be one of the following types: float32, float64. Shape is [..., M, M]. name: A name for the operation (optional). Returns: A Tensor. Has the same type as input. Shape is [...].

tf.errors.UnknownError.__init__(node_def, op, message, error_code=2) Creates an UnknownError.

tf.contrib.distributions.Chi2.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.WishartCholesky.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.