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support = Real() | torch.distributions#torch.distributions.cauchy.Cauchy.support |
property variance | torch.distributions#torch.distributions.cauchy.Cauchy.variance |
class torch.distributions.chi2.Chi2(df, validate_args=None) [source]
Bases: torch.distributions.gamma.Gamma Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to Gamma(alpha=0.5*df, beta=0.5) Example: >>> m = Chi2(torch.tensor([1.0]))
>>> m.sample() # Chi2 distributed with shape df=1
tensor([ 0.1046])
Parameters
df (float or Tensor) – shape parameter of the distribution
arg_constraints = {'df': GreaterThan(lower_bound=0.0)}
property df
expand(batch_shape, _instance=None) [source] | torch.distributions#torch.distributions.chi2.Chi2 |
arg_constraints = {'df': GreaterThan(lower_bound=0.0)} | torch.distributions#torch.distributions.chi2.Chi2.arg_constraints |
property df | torch.distributions#torch.distributions.chi2.Chi2.df |
expand(batch_shape, _instance=None) [source] | torch.distributions#torch.distributions.chi2.Chi2.expand |
torch.distributions.constraints.cat
alias of torch.distributions.constraints._Cat | torch.distributions#torch.distributions.constraints.cat |
class torch.distributions.constraints.Constraint [source]
Abstract base class for constraints. A constraint object represents a region over which a variable is valid, e.g. within which a variable can be optimized. Variables
~Constraint.is_discrete (bool) – Whether constrained space is discrete. Defaults to False.
~Constraint.event_dim (int) – Number of rightmost dimensions that together define an event. The check() method will remove this many dimensions when computing validity.
check(value) [source]
Returns a byte tensor of sample_shape + batch_shape indicating whether each event in value satisfies this constraint. | torch.distributions#torch.distributions.constraints.Constraint |
check(value) [source]
Returns a byte tensor of sample_shape + batch_shape indicating whether each event in value satisfies this constraint. | torch.distributions#torch.distributions.constraints.Constraint.check |
torch.distributions.constraints.dependent_property
alias of torch.distributions.constraints._DependentProperty | torch.distributions#torch.distributions.constraints.dependent_property |
torch.distributions.constraints.greater_than
alias of torch.distributions.constraints._GreaterThan | torch.distributions#torch.distributions.constraints.greater_than |
torch.distributions.constraints.greater_than_eq
alias of torch.distributions.constraints._GreaterThanEq | torch.distributions#torch.distributions.constraints.greater_than_eq |
torch.distributions.constraints.half_open_interval
alias of torch.distributions.constraints._HalfOpenInterval | torch.distributions#torch.distributions.constraints.half_open_interval |
torch.distributions.constraints.independent
alias of torch.distributions.constraints._IndependentConstraint | torch.distributions#torch.distributions.constraints.independent |
torch.distributions.constraints.integer_interval
alias of torch.distributions.constraints._IntegerInterval | torch.distributions#torch.distributions.constraints.integer_interval |
torch.distributions.constraints.interval
alias of torch.distributions.constraints._Interval | torch.distributions#torch.distributions.constraints.interval |
torch.distributions.constraints.less_than
alias of torch.distributions.constraints._LessThan | torch.distributions#torch.distributions.constraints.less_than |
torch.distributions.constraints.multinomial
alias of torch.distributions.constraints._Multinomial | torch.distributions#torch.distributions.constraints.multinomial |
torch.distributions.constraints.stack
alias of torch.distributions.constraints._Stack | torch.distributions#torch.distributions.constraints.stack |
class torch.distributions.constraint_registry.ConstraintRegistry [source]
Registry to link constraints to transforms.
register(constraint, factory=None) [source]
Registers a Constraint subclass in this registry. Usage: @my_registry.register(MyConstraintClass)
def construct_transform(constraint):
assert isinstance(constraint, MyConstraint)
return MyTransform(constraint.arg_constraints)
Parameters
constraint (subclass of Constraint) – A subclass of Constraint, or a singleton object of the desired class.
factory (callable) – A callable that inputs a constraint object and returns a Transform object. | torch.distributions#torch.distributions.constraint_registry.ConstraintRegistry |
register(constraint, factory=None) [source]
Registers a Constraint subclass in this registry. Usage: @my_registry.register(MyConstraintClass)
def construct_transform(constraint):
assert isinstance(constraint, MyConstraint)
return MyTransform(constraint.arg_constraints)
Parameters
constraint (subclass of Constraint) – A subclass of Constraint, or a singleton object of the desired class.
factory (callable) – A callable that inputs a constraint object and returns a Transform object. | torch.distributions#torch.distributions.constraint_registry.ConstraintRegistry.register |
class torch.distributions.continuous_bernoulli.ContinuousBernoulli(probs=None, logits=None, lims=(0.499, 0.501), validate_args=None) [source]
Bases: torch.distributions.exp_family.ExponentialFamily Creates a continuous Bernoulli distribution parameterized by probs or logits (but not both). The distribution is supported in [0, 1] and parameterized by ‘probs’ (in (0,1)) or ‘logits’ (real-valued). Note that, unlike the Bernoulli, ‘probs’ does not correspond to a probability and ‘logits’ does not correspond to log-odds, but the same names are used due to the similarity with the Bernoulli. See [1] for more details. Example: >>> m = ContinuousBernoulli(torch.tensor([0.3]))
>>> m.sample()
tensor([ 0.2538])
Parameters
probs (Number, Tensor) – (0,1) valued parameters
logits (Number, Tensor) – real valued parameters whose sigmoid matches ‘probs’ [1] The continuous Bernoulli: fixing a pervasive error in variational autoencoders, Loaiza-Ganem G and Cunningham JP, NeurIPS 2019. https://arxiv.org/abs/1907.06845
arg_constraints = {'logits': Real(), 'probs': Interval(lower_bound=0.0, upper_bound=1.0)}
cdf(value) [source]
entropy() [source]
expand(batch_shape, _instance=None) [source]
has_rsample = True
icdf(value) [source]
log_prob(value) [source]
logits [source]
property mean
property param_shape
probs [source]
rsample(sample_shape=torch.Size([])) [source]
sample(sample_shape=torch.Size([])) [source]
property stddev
support = Interval(lower_bound=0.0, upper_bound=1.0)
property variance | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli |
arg_constraints = {'logits': Real(), 'probs': Interval(lower_bound=0.0, upper_bound=1.0)} | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.arg_constraints |
cdf(value) [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.cdf |
entropy() [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.entropy |
expand(batch_shape, _instance=None) [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.expand |
has_rsample = True | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.has_rsample |
icdf(value) [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.icdf |
logits [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.logits |
log_prob(value) [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.log_prob |
property mean | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.mean |
property param_shape | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.param_shape |
probs [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.probs |
rsample(sample_shape=torch.Size([])) [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.rsample |
sample(sample_shape=torch.Size([])) [source] | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.sample |
property stddev | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.stddev |
support = Interval(lower_bound=0.0, upper_bound=1.0) | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.support |
property variance | torch.distributions#torch.distributions.continuous_bernoulli.ContinuousBernoulli.variance |
class torch.distributions.dirichlet.Dirichlet(concentration, validate_args=None) [source]
Bases: torch.distributions.exp_family.ExponentialFamily Creates a Dirichlet distribution parameterized by concentration concentration. Example: >>> m = Dirichlet(torch.tensor([0.5, 0.5]))
>>> m.sample() # Dirichlet distributed with concentrarion concentration
tensor([ 0.1046, 0.8954])
Parameters
concentration (Tensor) – concentration parameter of the distribution (often referred to as alpha)
arg_constraints = {'concentration': IndependentConstraint(GreaterThan(lower_bound=0.0), 1)}
entropy() [source]
expand(batch_shape, _instance=None) [source]
has_rsample = True
log_prob(value) [source]
property mean
rsample(sample_shape=()) [source]
support = Simplex()
property variance | torch.distributions#torch.distributions.dirichlet.Dirichlet |
arg_constraints = {'concentration': IndependentConstraint(GreaterThan(lower_bound=0.0), 1)} | torch.distributions#torch.distributions.dirichlet.Dirichlet.arg_constraints |
entropy() [source] | torch.distributions#torch.distributions.dirichlet.Dirichlet.entropy |
expand(batch_shape, _instance=None) [source] | torch.distributions#torch.distributions.dirichlet.Dirichlet.expand |
has_rsample = True | torch.distributions#torch.distributions.dirichlet.Dirichlet.has_rsample |
log_prob(value) [source] | torch.distributions#torch.distributions.dirichlet.Dirichlet.log_prob |
property mean | torch.distributions#torch.distributions.dirichlet.Dirichlet.mean |
rsample(sample_shape=()) [source] | torch.distributions#torch.distributions.dirichlet.Dirichlet.rsample |
support = Simplex() | torch.distributions#torch.distributions.dirichlet.Dirichlet.support |
property variance | torch.distributions#torch.distributions.dirichlet.Dirichlet.variance |
class torch.distributions.distribution.Distribution(batch_shape=torch.Size([]), event_shape=torch.Size([]), validate_args=None) [source]
Bases: object Distribution is the abstract base class for probability distributions.
property arg_constraints
Returns a dictionary from argument names to Constraint objects that should be satisfied by each argument of this distribution. Args that are not tensors need not appear in this dict.
property batch_shape
Returns the shape over which parameters are batched.
cdf(value) [source]
Returns the cumulative density/mass function evaluated at value. Parameters
value (Tensor) –
entropy() [source]
Returns entropy of distribution, batched over batch_shape. Returns
Tensor of shape batch_shape.
enumerate_support(expand=True) [source]
Returns tensor containing all values supported by a discrete distribution. The result will enumerate over dimension 0, so the shape of the result will be (cardinality,) + batch_shape + event_shape (where event_shape = () for univariate distributions). Note that this enumerates over all batched tensors in lock-step [[0, 0], [1, 1], …]. With expand=False, enumeration happens along dim 0, but with the remaining batch dimensions being singleton dimensions, [[0], [1], ... To iterate over the full Cartesian product use itertools.product(m.enumerate_support()). Parameters
expand (bool) – whether to expand the support over the batch dims to match the distribution’s batch_shape. Returns
Tensor iterating over dimension 0.
property event_shape
Returns the shape of a single sample (without batching).
expand(batch_shape, _instance=None) [source]
Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape. This method calls expand on the distribution’s parameters. As such, this does not allocate new memory for the expanded distribution instance. Additionally, this does not repeat any args checking or parameter broadcasting in __init__.py, when an instance is first created. Parameters
batch_shape (torch.Size) – the desired expanded size.
_instance – new instance provided by subclasses that need to override .expand. Returns
New distribution instance with batch dimensions expanded to batch_size.
icdf(value) [source]
Returns the inverse cumulative density/mass function evaluated at value. Parameters
value (Tensor) –
log_prob(value) [source]
Returns the log of the probability density/mass function evaluated at value. Parameters
value (Tensor) –
property mean
Returns the mean of the distribution.
perplexity() [source]
Returns perplexity of distribution, batched over batch_shape. Returns
Tensor of shape batch_shape.
rsample(sample_shape=torch.Size([])) [source]
Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched.
sample(sample_shape=torch.Size([])) [source]
Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.
sample_n(n) [source]
Generates n samples or n batches of samples if the distribution parameters are batched.
static set_default_validate_args(value) [source]
Sets whether validation is enabled or disabled. The default behavior mimics Python’s assert statement: validation is on by default, but is disabled if Python is run in optimized mode (via python -O). Validation may be expensive, so you may want to disable it once a model is working. Parameters
value (bool) – Whether to enable validation.
property stddev
Returns the standard deviation of the distribution.
property support
Returns a Constraint object representing this distribution’s support.
property variance
Returns the variance of the distribution. | torch.distributions#torch.distributions.distribution.Distribution |
property arg_constraints
Returns a dictionary from argument names to Constraint objects that should be satisfied by each argument of this distribution. Args that are not tensors need not appear in this dict. | torch.distributions#torch.distributions.distribution.Distribution.arg_constraints |
property batch_shape
Returns the shape over which parameters are batched. | torch.distributions#torch.distributions.distribution.Distribution.batch_shape |
cdf(value) [source]
Returns the cumulative density/mass function evaluated at value. Parameters
value (Tensor) – | torch.distributions#torch.distributions.distribution.Distribution.cdf |
entropy() [source]
Returns entropy of distribution, batched over batch_shape. Returns
Tensor of shape batch_shape. | torch.distributions#torch.distributions.distribution.Distribution.entropy |
enumerate_support(expand=True) [source]
Returns tensor containing all values supported by a discrete distribution. The result will enumerate over dimension 0, so the shape of the result will be (cardinality,) + batch_shape + event_shape (where event_shape = () for univariate distributions). Note that this enumerates over all batched tensors in lock-step [[0, 0], [1, 1], …]. With expand=False, enumeration happens along dim 0, but with the remaining batch dimensions being singleton dimensions, [[0], [1], ... To iterate over the full Cartesian product use itertools.product(m.enumerate_support()). Parameters
expand (bool) – whether to expand the support over the batch dims to match the distribution’s batch_shape. Returns
Tensor iterating over dimension 0. | torch.distributions#torch.distributions.distribution.Distribution.enumerate_support |
property event_shape
Returns the shape of a single sample (without batching). | torch.distributions#torch.distributions.distribution.Distribution.event_shape |
expand(batch_shape, _instance=None) [source]
Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape. This method calls expand on the distribution’s parameters. As such, this does not allocate new memory for the expanded distribution instance. Additionally, this does not repeat any args checking or parameter broadcasting in __init__.py, when an instance is first created. Parameters
batch_shape (torch.Size) – the desired expanded size.
_instance – new instance provided by subclasses that need to override .expand. Returns
New distribution instance with batch dimensions expanded to batch_size. | torch.distributions#torch.distributions.distribution.Distribution.expand |
icdf(value) [source]
Returns the inverse cumulative density/mass function evaluated at value. Parameters
value (Tensor) – | torch.distributions#torch.distributions.distribution.Distribution.icdf |
log_prob(value) [source]
Returns the log of the probability density/mass function evaluated at value. Parameters
value (Tensor) – | torch.distributions#torch.distributions.distribution.Distribution.log_prob |
property mean
Returns the mean of the distribution. | torch.distributions#torch.distributions.distribution.Distribution.mean |
perplexity() [source]
Returns perplexity of distribution, batched over batch_shape. Returns
Tensor of shape batch_shape. | torch.distributions#torch.distributions.distribution.Distribution.perplexity |
rsample(sample_shape=torch.Size([])) [source]
Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched. | torch.distributions#torch.distributions.distribution.Distribution.rsample |
sample(sample_shape=torch.Size([])) [source]
Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched. | torch.distributions#torch.distributions.distribution.Distribution.sample |
sample_n(n) [source]
Generates n samples or n batches of samples if the distribution parameters are batched. | torch.distributions#torch.distributions.distribution.Distribution.sample_n |
static set_default_validate_args(value) [source]
Sets whether validation is enabled or disabled. The default behavior mimics Python’s assert statement: validation is on by default, but is disabled if Python is run in optimized mode (via python -O). Validation may be expensive, so you may want to disable it once a model is working. Parameters
value (bool) – Whether to enable validation. | torch.distributions#torch.distributions.distribution.Distribution.set_default_validate_args |
property stddev
Returns the standard deviation of the distribution. | torch.distributions#torch.distributions.distribution.Distribution.stddev |
property support
Returns a Constraint object representing this distribution’s support. | torch.distributions#torch.distributions.distribution.Distribution.support |
property variance
Returns the variance of the distribution. | torch.distributions#torch.distributions.distribution.Distribution.variance |
class torch.distributions.exponential.Exponential(rate, validate_args=None) [source]
Bases: torch.distributions.exp_family.ExponentialFamily Creates a Exponential distribution parameterized by rate. Example: >>> m = Exponential(torch.tensor([1.0]))
>>> m.sample() # Exponential distributed with rate=1
tensor([ 0.1046])
Parameters
rate (float or Tensor) – rate = 1 / scale of the distribution
arg_constraints = {'rate': GreaterThan(lower_bound=0.0)}
cdf(value) [source]
entropy() [source]
expand(batch_shape, _instance=None) [source]
has_rsample = True
icdf(value) [source]
log_prob(value) [source]
property mean
rsample(sample_shape=torch.Size([])) [source]
property stddev
support = GreaterThan(lower_bound=0.0)
property variance | torch.distributions#torch.distributions.exponential.Exponential |
arg_constraints = {'rate': GreaterThan(lower_bound=0.0)} | torch.distributions#torch.distributions.exponential.Exponential.arg_constraints |
cdf(value) [source] | torch.distributions#torch.distributions.exponential.Exponential.cdf |
entropy() [source] | torch.distributions#torch.distributions.exponential.Exponential.entropy |
expand(batch_shape, _instance=None) [source] | torch.distributions#torch.distributions.exponential.Exponential.expand |
has_rsample = True | torch.distributions#torch.distributions.exponential.Exponential.has_rsample |
icdf(value) [source] | torch.distributions#torch.distributions.exponential.Exponential.icdf |
log_prob(value) [source] | torch.distributions#torch.distributions.exponential.Exponential.log_prob |
property mean | torch.distributions#torch.distributions.exponential.Exponential.mean |
rsample(sample_shape=torch.Size([])) [source] | torch.distributions#torch.distributions.exponential.Exponential.rsample |
property stddev | torch.distributions#torch.distributions.exponential.Exponential.stddev |
support = GreaterThan(lower_bound=0.0) | torch.distributions#torch.distributions.exponential.Exponential.support |
property variance | torch.distributions#torch.distributions.exponential.Exponential.variance |
class torch.distributions.exp_family.ExponentialFamily(batch_shape=torch.Size([]), event_shape=torch.Size([]), validate_args=None) [source]
Bases: torch.distributions.distribution.Distribution ExponentialFamily is the abstract base class for probability distributions belonging to an exponential family, whose probability mass/density function has the form is defined below pF(x;θ)=exp(⟨t(x),θ⟩−F(θ)+k(x))p_{F}(x; \theta) = \exp(\langle t(x), \theta\rangle - F(\theta) + k(x))
where θ\theta denotes the natural parameters, t(x)t(x) denotes the sufficient statistic, F(θ)F(\theta) is the log normalizer function for a given family and k(x)k(x) is the carrier measure. Note This class is an intermediary between the Distribution class and distributions which belong to an exponential family mainly to check the correctness of the .entropy() and analytic KL divergence methods. We use this class to compute the entropy and KL divergence using the AD framework and Bregman divergences (courtesy of: Frank Nielsen and Richard Nock, Entropies and Cross-entropies of Exponential Families).
entropy() [source]
Method to compute the entropy using Bregman divergence of the log normalizer. | torch.distributions#torch.distributions.exp_family.ExponentialFamily |
entropy() [source]
Method to compute the entropy using Bregman divergence of the log normalizer. | torch.distributions#torch.distributions.exp_family.ExponentialFamily.entropy |
class torch.distributions.fishersnedecor.FisherSnedecor(df1, df2, validate_args=None) [source]
Bases: torch.distributions.distribution.Distribution Creates a Fisher-Snedecor distribution parameterized by df1 and df2. Example: >>> m = FisherSnedecor(torch.tensor([1.0]), torch.tensor([2.0]))
>>> m.sample() # Fisher-Snedecor-distributed with df1=1 and df2=2
tensor([ 0.2453])
Parameters
df1 (float or Tensor) – degrees of freedom parameter 1
df2 (float or Tensor) – degrees of freedom parameter 2
arg_constraints = {'df1': GreaterThan(lower_bound=0.0), 'df2': GreaterThan(lower_bound=0.0)}
expand(batch_shape, _instance=None) [source]
has_rsample = True
log_prob(value) [source]
property mean
rsample(sample_shape=torch.Size([])) [source]
support = GreaterThan(lower_bound=0.0)
property variance | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor |
arg_constraints = {'df1': GreaterThan(lower_bound=0.0), 'df2': GreaterThan(lower_bound=0.0)} | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.arg_constraints |
expand(batch_shape, _instance=None) [source] | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.expand |
has_rsample = True | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.has_rsample |
log_prob(value) [source] | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.log_prob |
property mean | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.mean |
rsample(sample_shape=torch.Size([])) [source] | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.rsample |
support = GreaterThan(lower_bound=0.0) | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.support |
property variance | torch.distributions#torch.distributions.fishersnedecor.FisherSnedecor.variance |
class torch.distributions.gamma.Gamma(concentration, rate, validate_args=None) [source]
Bases: torch.distributions.exp_family.ExponentialFamily Creates a Gamma distribution parameterized by shape concentration and rate. Example: >>> m = Gamma(torch.tensor([1.0]), torch.tensor([1.0]))
>>> m.sample() # Gamma distributed with concentration=1 and rate=1
tensor([ 0.1046])
Parameters
concentration (float or Tensor) – shape parameter of the distribution (often referred to as alpha)
rate (float or Tensor) – rate = 1 / scale of the distribution (often referred to as beta)
arg_constraints = {'concentration': GreaterThan(lower_bound=0.0), 'rate': GreaterThan(lower_bound=0.0)}
entropy() [source]
expand(batch_shape, _instance=None) [source]
has_rsample = True
log_prob(value) [source]
property mean
rsample(sample_shape=torch.Size([])) [source]
support = GreaterThan(lower_bound=0.0)
property variance | torch.distributions#torch.distributions.gamma.Gamma |
arg_constraints = {'concentration': GreaterThan(lower_bound=0.0), 'rate': GreaterThan(lower_bound=0.0)} | torch.distributions#torch.distributions.gamma.Gamma.arg_constraints |
entropy() [source] | torch.distributions#torch.distributions.gamma.Gamma.entropy |
expand(batch_shape, _instance=None) [source] | torch.distributions#torch.distributions.gamma.Gamma.expand |
has_rsample = True | torch.distributions#torch.distributions.gamma.Gamma.has_rsample |
log_prob(value) [source] | torch.distributions#torch.distributions.gamma.Gamma.log_prob |
property mean | torch.distributions#torch.distributions.gamma.Gamma.mean |
rsample(sample_shape=torch.Size([])) [source] | torch.distributions#torch.distributions.gamma.Gamma.rsample |
support = GreaterThan(lower_bound=0.0) | torch.distributions#torch.distributions.gamma.Gamma.support |
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