class_docstr: Linear Projection block for predicting classification.
docstr: Applies Linear Projection to inputs.
  
  Args:
    inputs: Input data.
  
  Returns:
    Output of Linear Projection block.
docstr_tok: ["Applies","Linear","Projection","to","inputs",".","Args",":","inputs",":","Input","data",".","Returns",":","Output","of","Linear","Projection","block","."]
code: def __call__(self, inputs: jnp.ndarray) -> jnp.ndarray:
      """Applies Linear Projection to inputs.
  
      Args:
        inputs: Input data.
  
      Returns:
        Output of Linear Projection block.
      """
      bias_init_value = -math.log(
          (1 - self.prior_prob) / self.prior_prob
      )
      return nn.Dense(
          self.num_classes,
          kernel_init=pytorch_kernel_init(dtype=self.dtype),
          bias_init=nn.initializers.constant(bias_init_value),
          dtype=self.dtype,
      )(inputs)
  
imports: import math
  from typing import Any, Dict, Optional, Sequence, Tuple, Union
  from absl import logging
  import flax.linen
  import jax
  import jax.numpy
  import ml_collections
  from scenic.model_lib import matchers
  from scenic.model_lib.base_models import base_model
  from scenic.model_lib.base_models import box_utils
  from scenic.model_lib.base_models import model_utils
  from scenic.projects.baselines.deformable_detr.backbone import DeformableDETRBackbone
  from scenic.projects.baselines.deformable_detr.backbone import InputPosEmbeddingSine
  from scenic.projects.baselines.deformable_detr.backbone import mask_for_shape
  from scenic.projects.baselines.deformable_detr.deformable_transformer import BBoxCoordPredictor
  from scenic.projects.baselines.deformable_detr.deformable_transformer import DeformableDETRTransformer
  from scenic.projects.baselines.deformable_detr.deformable_transformer import inverse_sigmoid
  from scenic.projects.baselines.deformable_detr.deformable_transformer import pytorch_kernel_init
label_desc: Use image node_id 1 for calling the ObjectClassPredictor obj's underlying member method code with example usage: obj.__call__(inputs) and returns: nn