class_docstr: The Logistic distribution with location `loc` and `scale` parameters.
  
  #### Mathematical details
  
  The cumulative density function of this distribution is:
  
  ```none
  cdf(x; mu, sigma) = 1 / (1 + exp(-(x - mu) / sigma))
  ```
  
  where `loc = mu` and `scale = sigma`.
  
  The Logistic distribution is a member of the [location-scale family](
  https://en.wikipedia.org/wiki/Location-scale_family), i.e., it can be
  constructed as,
  
  ```none
  X ~ Logistic(loc=0, scale=1)
  Y = loc + scale * X
  ```
  
  #### Examples
  
  Examples of initialization of one or a batch of distributions.
  
  ```python
  tfd = tfp.distributions
  
  # Define a single scalar Logistic distribution.
  dist = tfd.Logistic(loc=0., scale=3.)
  
  # Evaluate the cdf at 1, returning a scalar.
  dist.cdf(1.)
  
  # Define a batch of two scalar valued Logistics.
  # The first has mean 1 and scale 11, the second 2 and 22.
  dist = tfd.Logistic(loc=[1, 2.], scale=[11, 22.])
  
  # Evaluate the pdf of the first distribution on 0, and the second on 1.5,
  # returning a length two tensor.
  dist.prob([0, 1.5])
  
  # Get 3 samples, returning a 3 x 2 tensor.
  dist.sample([3])
  
  # Arguments are broadcast when possible.
  # Define a batch of two scalar valued Logistics.
  # Both have mean 1, but different scales.
  dist = tfd.Logistic(loc=1., scale=[11, 22.])
  
  # Evaluate the pdf of both distributions on the same point, 3.0,
  # returning a length 2 tensor.
  dist.prob(3.0)
  ```
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code: def _event_shape_tensor(self):
      return tf.constant([], dtype=tf.int32)
  
imports: 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.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 prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 5 for calling the Logistic obj's underlying member method code with example usage: obj._event_shape_tensor() and returns: tf