class_docstr: A linear transformation applied over the last dimension of the input.
  
  Attributes:
    features: the number of output features.
    use_bias: whether to add a bias to the output (default: True).
    dtype: the dtype of the computation (default: infer from input and params).
    param_dtype: the dtype passed to parameter initializers (default: float32).
    precision: numerical precision of the computation see `jax.lax.Precision`
      for details.
    kernel_init: initializer function for the weight matrix.
    bias_init: initializer function for the bias.
docstr: Applies a linear transformation to the inputs along the last dimension.
  
  Args:
    inputs: The nd-array to be transformed.
  
  Returns:
    The transformed input.
docstr_tok: ["Applies","a","linear","transformation","to","the","inputs","along","the","last","dimension",".","Args",":","inputs",":","The","nd-array","to","be","transformed",".","Returns",":","The","transformed","input","."]
code: def __call__(self, inputs: Array) -> Array:
      """Applies a linear transformation to the inputs along the last dimension.
  
      Args:
        inputs: The nd-array to be transformed.
  
      Returns:
        The transformed input.
      """
      kernel = self.kernel
      bias = self.bias
  
      inputs, kernel, bias = dtypes.promote_dtype(
          inputs, kernel, bias, dtype=self.dtype
      )
      y = self.dot_general(
          inputs,
          kernel,
          (((inputs.ndim - 1,), (0,)), ((), ())),
          precision=self.precision,
      )
      if bias is not None:
          y += jnp.reshape(bias, (1,) * (y.ndim - 1) + (-1,))
      return y
  
imports: from __future__ import annotations
  import typing
  from types import MappingProxyType
  import jax
  import jax.numpy
  import numpy
  from jax import lax
  from flax.experimental import nnx
  from flax.experimental.nnx.nnx import rnglib, variables
  from flax.experimental.nnx.nnx.module import Module
  from flax.experimental.nnx.nnx.nn import dtypes, initializers
label_desc: Use image node_id 2 for calling the Linear obj's underlying member method code with example usage: obj.__call__(inputs) and returns: y