class_docstr: Initializes by sampling from a truncated normal distribution.
docstr: Constructs a :class:`TruncatedNormal` initializer.
  
  Args:
    stddev: The standard deviation parameter of the truncated
      normal distribution.
    mean: The mean of the truncated normal distribution.
    lower: Float or array representing the lower bound for truncation.
    upper: Float or array representing the upper bound for truncation.
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code: def __init__(
      self,
      stddev: Union[float, jax.Array] = 1.0,
      mean: Union[float, complex, jax.Array] = 0.0,
      lower: Union[float, jax.Array] = -2.0,
      upper: Union[float, jax.Array] = 2.0,
  ):
      """Constructs a :class:`TruncatedNormal` initializer.
  
      Args:
        stddev: The standard deviation parameter of the truncated
          normal distribution.
        mean: The mean of the truncated normal distribution.
        lower: Float or array representing the lower bound for truncation.
        upper: Float or array representing the upper bound for truncation.
      """
      self.stddev = stddev
      self.mean = mean
      self.lower = lower
      self.upper = upper
  
imports: from collections.abc import Sequence
  from typing import Any, Union
  from haiku._src import base
  from haiku._src.typing import Initializer
  import jax
  import jax.numpy
  import numpy
label_desc: Use image node_id 1 to create a new TruncatedNormal object from inherited base classes: hk.initializers with example: obj = TruncatedNormal(stddev, mean, lower, upper)