code: def __call__(self, *args, **kwargs):
      assert len(args) == 3
      x = args[0]
      k = args[1]
      dimension_numbers = args[2]
  
      # Use the `k.dtype` since it aligns with the `dtype` of its layers,
      # namely, the computation data type.
      comp_dtype = k.dtype
      x = jnp.asarray(x, comp_dtype)
  
      x_qdq = in_qdq(
          comp_dtype,
          x,
          self.input_scale.value,
          self.input_amax_history.value,
      )
      k_qdq = in_qdq(
          comp_dtype,
          k,
          self.kernel_scale.value,
          self.kernel_amax_history.value,
      )
      y_qdq = dot_general_with_precision(x_qdq, k_qdq, dimension_numbers)  # type: ignore
      y = out_qdq(
          comp_dtype,
          y_qdq,
          self.output_grad_scale.value,
          self.output_grad_amax_history.value,
      )
  
      return y
  
imports: import warnings
  from functools import partial
  from jax import custom_jvp, custom_vjp, lax, random
  from jax import numpy
  from flax.linen import initializers, module
label_desc: Use image node_id 2 for calling the Fp8DotGeneralOp obj's underlying member method code with example usage: obj.__call__() and returns: y