code: def _minimum_mean(samples, envelope, low, name=None):
      """Returns a stochastic lower bound on the mean of a scalar distribution.
  
      The idea is that if the true CDF is within an `eps`-envelope of the
      empirical CDF of the samples, and the support is bounded below, then
      the mean is bounded below as well.  In symbols,
  
      ```none
      sup_x(|F_n(x) - F(x)|) < eps
      ```
  
      The 0th dimension of `samples` is interpreted as independent and
      identically distributed samples.  The remaining dimensions are
      broadcast together with `envelope` and `low`, and operated on
      separately.
  
      Args:
        samples: Floating-point `Tensor` of samples from the distribution(s)
          of interest.  Entries are assumed IID across the 0th dimension.
          The other dimensions must broadcast with `envelope` and `low`.
        envelope: Floating-point `Tensor` of sizes of admissible CDF
          envelopes (i.e., the `eps` above).
        low: Floating-point `Tensor` of lower bounds on the distributions'
          supports.  `samples >= low`.
        name: A name for this operation (optional).
  
      Returns:
        bound: Floating-point `Tensor` of lower bounds on the true means.
  
      Raises:
        InvalidArgumentError: If some `sample` is found to be smaller than
          the corresponding `low`.
      """
      with tf.name_scope(name or "minimum_mean"):
          dtype = dtype_util.common_dtype(
              [samples, envelope, low], tf.float32
          )
          samples = tf.convert_to_tensor(
              value=samples, name="samples", dtype=dtype
          )
          envelope = tf.convert_to_tensor(
              value=envelope, name="envelope", dtype=dtype
          )
          low = tf.convert_to_tensor(value=low, name="low", dtype=dtype)
  
          xmin = tf.reduce_min(samples, axis=[0])
          msg = "Given sample minimum value falls below expectations"
          check_op = tf.debugging.assert_greater_equal(
              xmin, low, message=msg
          )
          with tf.control_dependencies([check_op]):
              return -_do_maximum_mean(-samples, envelope, -low)
  
imports: import functools
  import itertools
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.util.seed_stream import SeedStream
label_desc: Use image node_id 14 for calling a global function with example usage: _minimum_mean(samples, envelope, low, name) and returns: unknown