Disable sampling of transformed variables from `prior_predictive`

[ricardoV94]

This was added to facilitate sampling from the prior as a starting point for SMC, but with the new functions added in #4983 this is no longer needed.

pymc/pymc3/sampling.py

Lines 1983 to 1999 in 6d2aa5d

	# Any variables from var_names that are missing must be transformed variables.
	# Misspelled variables would have raised a KeyError above.
	missing_names = vars_.difference(names)
	for name in missing_names:
	transformed_value_var = model[name]
	rv_var = model.values_to_rvs[transformed_value_var]
	transform = transformed_value_var.tag.transform
	transformed_rv_var = transform.forward(rv_var, rv_var)
	names.append(name)
	vars_to_sample.append(transformed_rv_var)
	# If the user asked for the transformed variable in var_names, but not the
	# original RV, we add it manually here
	if rv_var.name not in names:
	names.append(rv_var.name)
	vars_to_sample.append(rv_var)

[markvrma]

I'd like to take up this issue.

[purna135]

Is it okay if I work on this?

[ricardoV94]

Is it okay if I work on this?

Sure

[purna135]

I need some assistance in replacing the following code with make_initial_point_fn

pymc/pymc/smc/smc.py

Lines 179 to 184 in e6fc2ec

	return sample_prior_predictive(
	self.draws,
	var_names=[v.name for v in self.model.unobserved_value_vars],
	model=self.model,
	return_inferencedata=False,
	)

pymc/pymc/initial_point.py

Line 130 in d84c718

def make_initial_point_fn(