Remove unused arguments from SobolSensitivityGPMean (facebook#3892)

Summary:
Pull Request resolved: facebook#3892

Two arguments are no longer used, one as a result of D75712208 and one has long been unused

Reviewed By: saitcakmak

Differential Revision: D76151380

fbshipit-source-id: f9572bb76282b90001c1c50bbe173900bc8e4b11

Author: esantorella

ax/utils/sensitivity/sobol_measures.py

@@ -450,11 +450,9 @@ def __init__(
         second_order: bool = False,
         input_qmc: bool = False,
         num_bootstrap_samples: int = 1,
-        first_order_idcs: torch.Tensor | None = None,
         link_function: Callable[
             [torch.Tensor, torch.Tensor], torch.Tensor
         ] = GaussianLinkMean,
-        mini_batch_size: int = 128,
         discrete_features: list[int] | None = None,
     ) -> None:
         r"""Computes three types of Sobol indices:
@@ -470,13 +468,9 @@ def __init__(
             input_qmc: If True, a qmc Sobol grid is use instead of uniformly random.
             num_bootstrap_samples: If bootstrap is true, the number of bootstraps has
                 to be specified.
-            first_order_idcs: Tensor of previously computed first order indices, where
-                first_order_idcs.shape = torch.Size([dim]).
             link_function: The link function to be used when computing the indices.
                 Indices can be computed for the mean or on samples of the posterior,
                 predictive, but defaults to computing on the mean (GaussianLinkMean).
-            mini_batch_size: The size of the mini-batches used while evaluating the
-                model posterior. Increasing this will increase the memory usage.
             discrete_features: If specified, the inputs associated with the indices in
                 this list are generated using an integer-valued uniform distribution,
                 rather than the default (pseudo-)random continuous uniform distribution.
@@ -903,9 +897,6 @@ def ax_parameter_sens(
         **sobol_kwargs,
     )
     feature_names = digest.feature_names
-    indices_unsigned = array_with_string_indices_to_dict(
-        rows=metrics, cols=feature_names, A=ind.cpu().numpy()
-    )
     if signed:
         ind_deriv = compute_derivatives_from_model_list(
             model_list=model_list,
@@ -932,7 +923,6 @@ def ax_parameter_sens(
             bounds=bounds,
             order="second",
             discrete_features=digest.categorical_features + digest.ordinal_features,
-            first_order_idcs=indices_unsigned,
             **sobol_kwargs,
         )
         second_order_feature_names = [