code: def make_causal_mask(
      x: Array, extra_batch_dims: int = 0, dtype: Dtype = jnp.float32
  ) -> Array:
      """Make a causal mask for self-attention.
  
      In case of 1d inputs (i.e., ``[batch..., len]``, the self-attention weights
      will be ``[batch..., heads, len, len]`` and this function will produce a
      causal mask of shape ``[batch..., 1, len, len]``.
  
      Args:
        x: input array of shape ``[batch..., len]``
        extra_batch_dims: number of batch dims to add singleton axes for, none by
          default
        dtype: mask return dtype
  
      Returns:
        A ``[batch..., 1, len, len]`` shaped causal mask for 1d attention.
      """
      idxs = jnp.broadcast_to(
          jnp.arange(x.shape[-1], dtype=jnp.int32), x.shape
      )
      return make_attention_mask(
          idxs,
          idxs,
          jnp.greater_equal,
          extra_batch_dims=extra_batch_dims,
          dtype=dtype,
      )
  
imports: import functools
  import warnings
  from typing import Any, Callable, Optional, Tuple, Union, overload
  import jax
  import jax.numpy
  from jax import lax, random
  from flax.linen import initializers
  from flax.linen.dtypes import promote_dtype
  from flax.linen.linear import DenseGeneral, DotGeneralT, PrecisionLike, default_kernel_init
  from flax.linen.module import Module, compact, merge_param
  from flax.linen.normalization import LayerNorm
label_desc: Use image node_id 4 for calling a global function with example usage: make_causal_mask(x, extra_batch_dims, dtype) and returns: make_attention_mask