class_docstr: Linear Projection block for predicting classification.
class_docstr_tok: ["Linear","Projection","block","for","predicting","classification","."]
docstr: 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_range = 1.0 / np.sqrt(inputs.shape[-1])
      return nn.Dense(
          self.num_classes,
          kernel_init=pytorch_kernel_init(dtype=self.dtype),
          bias_init=uniform_initializer(
              -bias_range, bias_range, self.dtype
          ),
          dtype=self.dtype,
      )(inputs)
  
imports: import functools
  from typing import Any, Callable, Dict, Tuple, Optional
  import flax.linen
  import jax
  from jax.nn import initializers
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.model_lib.layers import attention_layers
  from scenic.projects.baselines import resnet
  from scenic.projects.baselines.detr import detr_base_model
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