def __init__(self,<EOL>df,<EOL>loc=None,<EOL>scale_identity_multiplier=None,<EOL>scale_diag=None,<EOL>scale_tril=None,<EOL>scale_perturb_factor=None,<EOL>scale_perturb_diag=None,<EOL>validate_args=False,<EOL>allow_nan_stats=True,<EOL>name="<STR_LIT>"):
parameters = dict(locals())<EOL>graph_parents = [df, loc, scale_identity_multiplier, scale_diag,<EOL>scale_tril, scale_perturb_factor, scale_perturb_diag]<EOL>with tf.name_scope(name) as name:<EOL><INDENT>with tf.name_scope("<STR_LIT>"):<EOL><INDENT>dtype = dtype_util.common_dtype(graph_parents, tf.float32)<EOL>df = tf.convert_to_tensor(value=df, name="<STR_LIT>", dtype=dtype)<EOL>affine = affine_bijector.Affine(<EOL>shift=loc,<EOL>scale_identity_multiplier=scale_identity_multiplier,<EOL>scale_diag=scale_diag,<EOL>scale_tril=scale_tril,<EOL>scale_perturb_factor=scale_perturb_factor,<EOL>scale_perturb_diag=scale_perturb_diag,<EOL>validate_args=validate_args,<EOL>dtype=dtype)<EOL>distribution = student_t.StudentT(<EOL>df=df,<EOL>loc=tf.zeros([], dtype=affine.dtype),<EOL>scale=tf.ones([], dtype=affine.dtype))<EOL>batch_shape, override_event_shape = (<EOL>distribution_util.shapes_from_loc_and_scale(<EOL>affine.shift, affine.scale))<EOL>override_batch_shape = distribution_util.pick_vector(<EOL>distribution.is_scalar_batch(), batch_shape,<EOL>tf.constant([], dtype=tf.int32))<EOL>super(_VectorStudentT, self).__init__(<EOL>distribution=distribution,<EOL>bijector=affine,<EOL>batch_shape=override_batch_shape,<EOL>event_shape=override_event_shape,<EOL>validate_args=validate_args,<EOL>name=name)<EOL>self._parameters = parameters<EOL><DEDENT><DEDENT>
Instantiates the vector Student's t-distributions on `R^k`.

        The `batch_shape` is the broadcast between `df.batch_shape` and
        `Affine.batch_shape` where `Affine` is constructed from `loc` and
        `scale_*` arguments.

        The `event_shape` is the event shape of `Affine.event_shape`.

        Args:
          df: Floating-point `Tensor`. The degrees of freedom of the
            distribution(s). `df` must contain only positive values. Must be
            scalar if `loc`, `scale_*` imply non-scalar batch_shape or must have the
            same `batch_shape` implied by `loc`, `scale_*`.
          loc: Floating-point `Tensor`. If this is set to `None`, no `loc` is
            applied.
          scale_identity_multiplier: floating point rank 0 `Tensor` representing a
            scaling done to the identity matrix. When `scale_identity_multiplier =
            scale_diag=scale_tril = None` then `scale += IdentityMatrix`. Otherwise
            no scaled-identity-matrix is added to `scale`.
          scale_diag: Floating-point `Tensor` representing the diagonal matrix.
            `scale_diag` has shape [N1, N2, ..., k], which represents a k x k
            diagonal matrix. When `None` no diagonal term is added to `scale`.
          scale_tril: Floating-point `Tensor` representing the diagonal matrix.
            `scale_diag` has shape [N1, N2, ..., k, k], which represents a k x k
            lower triangular matrix. When `None` no `scale_tril` term is added to
            `scale`. The upper triangular elements above the diagonal are ignored.
          scale_perturb_factor: Floating-point `Tensor` representing factor matrix
            with last two dimensions of shape `(k, r)`. When `None`, no rank-r
            update is added to `scale`.
          scale_perturb_diag: Floating-point `Tensor` representing the diagonal
            matrix. `scale_perturb_diag` has shape [N1, N2, ..., r], which
            represents an r x r Diagonal matrix. When `None` low rank updates will
            take the form `scale_perturb_factor * scale_perturb_factor.T`.
          validate_args: Python `bool`, default `False`. When `True` distribution
            parameters are checked for validity despite possibly degrading runtime
            performance. When `False` invalid inputs may silently render incorrect
            outputs.
          allow_nan_stats: Python `bool`, default `True`. When `True`,
            statistics (e.g., mean, mode, variance) use the value "`NaN`" to
            indicate the result is undefined. When `False`, an exception is raised
            if one or more of the statistic's batch members are undefined.
          name: Python `str` name prefixed to Ops created by this class.