class_docstr: The log-normal distribution.
class_docstr_tok: ["The","log-normal","distribution","."]
docstr: Constructs a LogNormal from its mean and variance.
  
  **Experimental: Naming, location of this API may change.**
  
  Args:
    mean: The mean of the constructed distribution. Must be greater than 0.
    variance: The variance of the distribution. Must be greater than 0.
    **kwargs: Other keyword arguments passed directly to `__init__`, e.g.
      `validate_args`.
  
  Returns:
    lognormal: A distribution with the given parameterization.
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code: def experimental_from_mean_variance(cls, mean, variance, **kwargs):
      """Constructs a LogNormal from its mean and variance.
  
      **Experimental: Naming, location of this API may change.**
  
      Args:
        mean: The mean of the constructed distribution. Must be greater than 0.
        variance: The variance of the distribution. Must be greater than 0.
        **kwargs: Other keyword arguments passed directly to `__init__`, e.g.
          `validate_args`.
  
      Returns:
        lognormal: A distribution with the given parameterization.
      """
      dtype = dtype_util.common_dtype(
          [mean, variance], dtype_hint=tf.float32
      )
      mean = tensor_util.convert_nonref_to_tensor(mean, dtype=dtype)
      variance = tensor_util.convert_nonref_to_tensor(
          variance, dtype=dtype
      )
  
      scale = DeferredTensor(
          mean,
          lambda mean: tf.sqrt(tf.math.log1p(variance / mean**2)),
      )
      loc = DeferredTensor(
          mean, lambda mean: tf.math.log(mean) - scale**2 / 2.0
      )
      return cls(loc=loc, scale=scale, **kwargs)
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.util.deferred_tensor import DeferredTensor
label_desc: Use image node_id 5 for calling the LogNormal obj's underlying member method code with example usage: obj.experimental_from_mean_variance(cls, mean, variance) and returns: cls