code: def dense_general(
      scope,
      inputs,
      features,
      axis=-1,
      batch_dims=(),
      bias=True,
      dtype=jnp.float32,
      kernel_init=default_kernel_init,
      bias_init=initializers.zeros_init(),
      precision=None,
  ):
      """Applies a linear transformation to the inputs along multiple dimensions.
  
      Args:
        inputs: The nd-array to be transformed.
        features: tuple with numbers of output features.
        axis: tuple with axes to apply the transformation on.
        batch_dims: tuple with batch axes.
        bias: whether to add a bias to the output (default: True).
        dtype: the dtype of the computation (default: float32).
        kernel_init: initializer function for the weight matrix.
        bias_init: initializer function for the bias.
        precision: numerical precision of the computation see `jax.lax.Precision`
          for details.
      Returns:
        The transformed input.
      """
      inputs = jnp.asarray(inputs, dtype)
  
      if not isinstance(features, Iterable):
          features = (features,)
      if not isinstance(axis, Iterable):
          axis = (axis,)
      if not isinstance(batch_dims, Iterable):
          batch_dims = (batch_dims,)
      features, axis, batch_dims = (
          tuple(features),
          tuple(axis),
          tuple(batch_dims),
      )
  
      if batch_dims:
          max_dim = np.max(batch_dims)
          if set(batch_dims) != set(range(max_dim + 1)):
              raise ValueError(
                  "batch_dims %s must be consecutive leading "
                  "dimensions starting from 0." % str(batch_dims)
              )
  
      ndim = inputs.ndim
      n_batch_dims = len(batch_dims)
      axis = _normalize_axes(axis, ndim)
      batch_dims = _normalize_axes(batch_dims, ndim)
      n_axis, n_features = len(axis), len(features)
  
      def kernel_init_wrap(rng, shape, dtype=jnp.float32):
          size_batch_dims = np.prod(
              shape[:n_batch_dims], dtype=np.int32
          )
          flat_shape = (
              np.prod(shape[n_batch_dims : n_axis + n_batch_dims]),
              np.prod(shape[-n_features:]),
          )
          kernel = jnp.concatenate(
              [
                  kernel_init(rng, flat_shape, dtype)
                  for _ in range(size_batch_dims)
              ],
              axis=0,
          )
          return jnp.reshape(kernel, shape)
  
      batch_shape = tuple(inputs.shape[ax] for ax in batch_dims)
      kernel_shape = tuple(inputs.shape[ax] for ax in axis) + features
      kernel = scope.param(
          "kernel", kernel_init_wrap, batch_shape + kernel_shape
      )
      kernel = jnp.asarray(kernel, dtype)
  
      batch_ind = tuple(range(n_batch_dims))
      contract_ind = tuple(range(n_batch_dims, n_axis + n_batch_dims))
      out = lax.dot_general(
          inputs,
          kernel,
          ((axis, contract_ind), (batch_dims, batch_ind)),
          precision=precision,
      )
      if bias:
  
          def bias_init_wrap(rng, shape, dtype=jnp.float32):
              size_batch_dims = np.prod(
                  shape[:n_batch_dims], dtype=np.int32
              )
              flat_shape = (np.prod(shape[-n_features:]),)
              bias = jnp.concatenate(
                  [
                      bias_init(rng, flat_shape, dtype)
                      for _ in range(size_batch_dims)
                  ],
                  axis=0,
              )
              return jnp.reshape(bias, shape)
  
          bias = scope.param(
              "bias", bias_init_wrap, batch_shape + features
          )
  
          # Reshape bias for broadcast.
          expand_dims = sorted(
              set(range(inputs.ndim)) - set(axis) - set(batch_dims)
          )
          for ax in expand_dims:
              bias = jnp.expand_dims(bias, ax)
          bias = jnp.asarray(bias, dtype)
          out = out + bias
      return out
  
file_path: flax/flax/core/nn/linear.py
imports: from collections.abc import Iterable
  import jax.numpy
  import numpy
  from jax import lax
  from flax import struct
  from flax.core import Scope
  from flax.linen import initializers
label_desc: Use image node_id 2 for calling a global function with example usage: dense_general(scope, inputs, features, axis, batch_dims, bias, dtype, kernel_init, bias_init, precision) and returns: out, jnp, jnp