class_docstr: Module component with similar interface to numpy.random
  (numpy.random.RandomState).
  
  Parameters
  ----------
  seed : int or list of 6 int
      A default seed to initialize the random state.
      If a single int is given, it will be replicated 6 times.
      The first 3 values of the seed must all be less than M1 = 2147483647,
      and not all 0; and the last 3 values must all be less than
      M2 = 2147462579, and not all 0.
docstr: Sample a tensor of values from a symmetrically truncated normal distribution.
  
  Parameters
  ----------
  size : int_vector_like
      Array dimensions for the output tensor.
  avg : float_like, optional
      The mean value for the truncated normal to sample from (defaults to 0.0).
  std : float_like, optional
      The standard deviation for the truncated normal to sample from (defaults to 1.0).
  ndim : int, optional
      The number of dimensions for the output tensor (defaults to None).
      This argument is necessary if the size argument is ambiguous on the number of dimensions.
  dtype : str, optional
      The data-type for the output tensor. If not specified,
      the dtype is inferred from avg and std, but it is at least as precise as floatX.
  kwargs
      Other keyword arguments for random number generation (see uniform).
  
  Returns
  -------
  samples : TensorVariable
      A Theano tensor of samples randomly drawn from a truncated normal distribution.
  
  See Also
  --------
  normal
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code: def truncated_normal(
      self,
      size,
      avg=0.0,
      std=1.0,
      ndim=None,
      dtype=None,
      nstreams=None,
      **kwargs,
  ):
      """
      Sample a tensor of values from a symmetrically truncated normal distribution.
  
      Parameters
      ----------
      size : int_vector_like
          Array dimensions for the output tensor.
      avg : float_like, optional
          The mean value for the truncated normal to sample from (defaults to 0.0).
      std : float_like, optional
          The standard deviation for the truncated normal to sample from (defaults to 1.0).
      ndim : int, optional
          The number of dimensions for the output tensor (defaults to None).
          This argument is necessary if the size argument is ambiguous on the number of dimensions.
      dtype : str, optional
          The data-type for the output tensor. If not specified,
          the dtype is inferred from avg and std, but it is at least as precise as floatX.
      kwargs
          Other keyword arguments for random number generation (see uniform).
  
      Returns
      -------
      samples : TensorVariable
          A Theano tensor of samples randomly drawn from a truncated normal distribution.
  
      See Also
      --------
      normal
      """
      # constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.)
      std = std / tensor.constant(0.87962566103423978)
      return self.normal(
          size=size,
          avg=avg,
          std=std,
          truncate=True,
          ndim=ndim,
          dtype=dtype,
          nstreams=nstreams,
          **kwargs,
      )
  
imports: from __future__ import absolute_import, print_function, division
  import warnings
  import numpy
  from six import integer_types, string_types
  from six.moves import xrange
  import theano
  from theano import Op, Apply, shared, config, Variable
  from theano import gradient, function
  from theano.gradient import undefined_grad
  from theano import tensor
  from theano.tensor import TensorType, as_tensor_variable, get_vector_length, cast, opt, scal
  from theano.compile import optdb
  from theano.gof import local_optimizer, ParamsType
  from theano.scalar import bool, int32
  from .None import multinomial
label_desc: Use image node_id 15 for calling the MRG_RandomStreams obj's underlying member method code with example usage: obj.truncated_normal(size, avg, std, ndim, dtype, nstreams) and returns: self