code: def stdtr(df, t, name=None):
      """Compute cumulative distribution function of Student T distribution.
  
      This function returns the integral from minus infinity to `t` of Student T
      distribution with `df > 0` degrees of freedom.
  
      Args:
        df: Floating-point `Tensor`. The degrees of freedom of the
          distribution(s). `df` must contain only positive values.
        t: Floating-point `Tensor`. Where to compute the cumulative
          distribution function.
        name: A name for the operation (optional).
  
      Returns:
        A `Tensor` with shape broadcast according to the arguments.
  
      Raises:
        TypeError: if `df` is not one of the following types: `float32`, `float64`.
      """
      with tf.name_scope(name or "stdtr"):
          dtype = dtype_util.common_dtype([df, t], tf.float32)
          df = tf.convert_to_tensor(df, dtype=dtype)
          t = tf.convert_to_tensor(t, dtype=dtype)
  
          if dtype_util.as_numpy_dtype(dtype) not in [
              np.float32,
              np.float64,
          ]:
              raise TypeError(
                  f"df.dtype={dtype} is not handled. "
                  "See docstring for supported types."
              )
  
          return _stdtr_custom_gradient(df, t)
  
imports: import functools
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import custom_gradient
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import special_math
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math import generic
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math import numeric
  from tensorflow_probability.python.math import special
label_desc: Use image node_id 11 for calling a global function with example usage: stdtr(df, t, name) and returns: _stdtr_custom_gradient