Examples

This page lists public example exports and their validation status. Each testcase link opens a representative ONNX model in Netron; the table is generated from example metadata and tests so it should be treated as reference data, not as a hand-maintained checklist.

Component	Description	Testcases	Since
MlpExample	A simple Equinox MLP (converter pipeline).	mlp_training_mode ✅ mlp_training_mode_f64 ✅ mlp_inference_mode ✅ mlp_inference_mode_f64 ✅ mlp_batched_training_mode ✅ mlp_batched_training_mode_f64 ✅	0.8.0
SimpleLinearExample	A simple linear layer example using Equinox (converter).	simple_linear ✅ simple_linear_f64 ✅ nn_linear ✅ nn_linear_f64 ✅	0.7.1
Attention	Multi-Head Self-Attention using Equinox modules.	attention_dynamic ✅ attention ✅	0.10.0
AttentionCore	Multi-Head Self-Attention without rotary processing.	attention_core_dynamic ✅ attention_core ✅	0.10.0
Block	Transformer Block.	transformer_block_dynamic ✅ transformer_block ✅	0.10.0
DINOv3VisionTransformer	DINOv3 Vision Transformer	eqx_dinov3_vit_Ti14_dynamic ✅ eqx_dinov3_vit_Ti14 ✅ eqx_dinov3_vit_S14_dynamic ✅ eqx_dinov3_vit_S14 ✅ eqx_dinov3_vit_B14_dynamic ✅ eqx_dinov3_vit_B14 ✅ eqx_dinov3_vit_S16_dynamic ✅ eqx_dinov3_vit_S16 ✅	0.10.0
PatchEmbed	Image to Patch Embedding.	patch_embed ✅	0.10.0
AttentionBlock	Self-attention block with rotary embeddings and sinks.	gpt_oss_attention_block ✅	0.10.2
MLPBlock	Mixture-of-experts SwiGLU feed-forward block.	gpt_oss_mlp_block ✅	0.10.2
RMSNorm	Root mean square normalisation used by GPT-OSS.	gpt_oss_rmsnorm_dynamic ✅ gpt_oss_rmsnorm ✅	0.10.2
Transformer	Full GPT-OSS Transformer stack.	gpt_oss_transformer ✅	0.10.2
TransformerBlock	GPT-OSS Transformer layer (attention + MoE).	gpt_oss_transformer_block_dynamic ✅ gpt_oss_transformer_block ✅	0.10.2
GPT	A simple GPT model that reuses nnx.MultiHeadAttention.	gpt_dynamic ✅ gpt ✅	0.7.0
GPT_Attention	A multi-head attention layer.	gpt_attention ✅	0.7.1
GPT_CausalSelfAttention	A causal self-attention module.	gpt_causal_self_attention_dynamic ✅ gpt_causal_self_attention ✅	0.7.0
GPT_Embeddings	Combines token and position embeddings with dropout.	gpt_embeddings_dynamic ✅ gpt_embeddings ✅	0.7.0
GPT_Head	The head of the GPT model.	gpt_head_dynamic ✅ gpt_head ✅	0.7.0
GPT_MLP	An MLP block with GELU activation from nanoGPT.	gpt_mlp_dynamic ✅ gpt_mlp ✅	0.7.0
GPT_PositionEmbedding	A positional embedding layer using nnx.Embed.	gpt_position_embedding ✅	0.7.0
GPT_TokenEmbedding	A token embedding layer using nnx.Embed.	gpt_token_embedding_dynamic ✅ gpt_token_embedding ✅	0.7.0
GPT_TransformerBlock	A transformer block combining attention and MLP.	gpt_block_dynamic ✅ gpt_block ✅	0.7.0
GPT_TransformerStack	A stack of transformer blocks.	gpt_transformer_stack_dynamic ✅ gpt_transformer_stack ✅	0.7.0
GPT_broadcast_add	Simple dynamic broadcast + add	gpt_broadcast_add_dynamic_dynamic ✅ gpt_broadcast_add_dynamic_dynamic_f64 ✅ gpt_broadcast_add_dynamic ✅ gpt_broadcast_add_dynamic_f64 ✅	0.7.0
cfl_timestep	Tests the CFL condition timestep calculation.	cfl_timestep_f64 ✅	0.6.5
weno_reconstruction	Tests the complex arithmetic pattern found in WENO schemes.	weno_reconstruction_f64 ✅	0.6.5
fori_loop_test	fori_loop_test: demonstrates jax.lax.fori_loop with a simple loop.	fori_loop_test ✅ fori_loop_test_f64 ✅	0.6.3
issue18_abs	Test jnp.abs from issue 18	abs_fn ✅ abs_fn_f64 ✅	0.6.3
issue18_arange	Test jnp.arange from issue 18	arange_fn ✅	0.6.3
issue18_fori_loop	Test jax.lax.fori_loop from issue 18	fori_loop_fn ✅ fori_loop_fn_f64 ✅	0.6.3
issue18_linspace	Test jnp.linspace from issue 18	linspace_fn ✅	0.6.3
issue18_scan	Test jax.lax.scan from issue 18 (no xs)	scan_fn ✅	0.6.3
issue18_sign	Test jnp.sign from issue 18	sign_fn ✅ sign_fn_f64 ✅	0.6.3
issue18_where	Test jnp.where from issue 18	where_fn ✅ where_fn_f64 ✅	0.6.3
issue18_while_loop	Test jax.lax.while_loop from issue 18	while_loop_fn ✅	0.9.0
select_test	Demonstrates jnp.select with scalar and tensor predicates.	select_test_all_options ✅ select_test_scalar_select_option_0 ✅ select_test_scalar_select_option_1 ✅ select_test_scalar_select_option_2 ✅ select_test_default_case ✅	0.9.0
sort_test	sort_test: demonstrates jnp.sort on slices of an input array.	sort_test_basic ✅	0.9.0
cond_scatter_add_mul	Scatter add/mul inside conditional branches (converter).	cond_scatter_add_mul_f64_a ✅ cond_scatter_add_mul_f64_b ✅	0.8.0
cond_scatter_repro	Reproduces a bug where lax.cond subgraphs do not inherit parent initializers.	cond_scatter_repro_f64 ✅	0.6.4
remat2	Tests a simple case of jax.checkpoint (also known as jax.remat2).	checkpoint_scalar_f32 ✅ checkpoint_scalar_f32_f64 ✅	0.6.5
scatter_window	Window-scatter (H×W patch) with implicit batch (depth-3 path). Exercises GatherScatterMode.FILL_OR_DROP and double precision. Regression of a prior conversion failure.	scatter_window_update_f64_example ✅	0.7.4
two_times_silu	Regression for calling jax.nn.silu twice (issue #139).	two_times_silu_scalar ✅ two_times_silu_scalar_f64 ✅	0.10.2
LinenCNN	A simple convolutional neural network (CNN).	simple_cnn_static ✅ simple_cnn_dynamic ✅	0.11.0
LinenMLP	A simple Linen MLP with BatchNorm, Dropout, and GELU activation.	simple_linen_mlp_static ✅ simple_linen_mlp_static_f64 ✅ simple_linen_mlp_dynamic ✅ simple_linen_mlp_dynamic_f64 ✅ simple_linen_mlp_with_call_params_dynamic ✅ simple_linen_mlp_with_call_params_dynamic_f64 ✅ simple_linen_mlp_with_call_params ✅ simple_linen_mlp_with_call_params_f64 ✅	0.11.0
LinenMLPSequential	A Linen MLP built from flax.linen.Sequential.	simple_linen_mlp_sequential_static ✅ simple_linen_mlp_sequential_static_f64 ✅ simple_linen_mlp_sequential_dynamic ✅ simple_linen_mlp_sequential_dynamic_f64 ✅	0.11.0
MaxDiffusion_base14	MaxDiffusion UNet: base14	maxdiffusion_base14 ✅	0.12.4
MaxDiffusion_base21	MaxDiffusion UNet: base21	maxdiffusion_base21 ✅	0.12.4
MaxDiffusion_base_2_base	MaxDiffusion UNet: base_2_base	maxdiffusion_base_2_base ✅	0.12.4
MaxDiffusion_base_xl	MaxDiffusion UNet: base_xl	maxdiffusion_base_xl ✅	0.12.4
MaxDiffusion_base_xl_lightning	MaxDiffusion UNet: base_xl_lightning	maxdiffusion_base_xl_lightning ✅	0.12.4
MaxText_deepseek2_16b	MaxText model: deepseek2-16b	maxtext_deepseek2-16b ✅	0.11.1
MaxText_deepseek2_236b	MaxText model: deepseek2-236b	maxtext_deepseek2-236b ✅	0.11.1
MaxText_deepseek3_2_671b	MaxText model: deepseek3.2-671b	maxtext_deepseek3.2-671b ✅	0.11.1
MaxText_deepseek3_671b	MaxText model: deepseek3-671b	maxtext_deepseek3-671b ✅	0.11.1
MaxText_deepseek3_671b_2dfsdp	MaxText model: deepseek3-671b-2dfsdp	maxtext_deepseek3-671b-2dfsdp ✅	0.11.1
MaxText_deepseek3_test	MaxText model: deepseek3-test	maxtext_deepseek3-test ✅	0.11.1
MaxText_deepseek3_tiny	MaxText model: deepseek3-tiny	maxtext_deepseek3-tiny ✅	0.11.1
MaxText_gemma2_27b	MaxText model: gemma2-27b	maxtext_gemma2-27b ✅	0.11.1
MaxText_gemma2_2b	MaxText model: gemma2-2b	maxtext_gemma2-2b ✅	0.11.1
MaxText_gemma2_9b	MaxText model: gemma2-9b	maxtext_gemma2-9b ✅	0.11.1
MaxText_gemma3_12b	MaxText model: gemma3-12b	maxtext_gemma3-12b ✅	0.11.1
MaxText_gemma3_27b	MaxText model: gemma3-27b	maxtext_gemma3-27b ✅	0.11.1
MaxText_gemma3_4b	MaxText model: gemma3-4b	maxtext_gemma3-4b ✅	0.11.1
MaxText_gemma_2b	MaxText model: gemma-2b	maxtext_gemma-2b ✅	0.11.1
MaxText_gemma_7b	MaxText model: gemma-7b	maxtext_gemma-7b ✅	0.11.1
MaxText_gpt3_175b	MaxText model: gpt3-175b	maxtext_gpt3-175b ✅	0.11.1
MaxText_gpt3_22b	MaxText model: gpt3-22b	maxtext_gpt3-22b ✅	0.11.1
MaxText_gpt3_52k	MaxText model: gpt3-52k	maxtext_gpt3-52k ✅	0.11.1
MaxText_gpt3_6b	MaxText model: gpt3-6b	maxtext_gpt3-6b ✅	0.11.1
MaxText_kimi_k2_1t	MaxText model: kimi-k2-1t	maxtext_kimi-k2-1t ✅	0.11.1
MaxText_llama2_13b	MaxText model: llama2-13b	maxtext_llama2-13b ✅	0.11.1
MaxText_llama2_70b	MaxText model: llama2-70b	maxtext_llama2-70b ✅	0.11.1
MaxText_llama2_7b	MaxText model: llama2-7b	maxtext_llama2-7b ✅	0.11.1
MaxText_llama3_1_405b	MaxText model: llama3.1-405b	maxtext_llama3.1-405b ✅	0.11.1
MaxText_llama3_1_70b	MaxText model: llama3.1-70b	maxtext_llama3.1-70b ✅	0.11.1
MaxText_llama3_1_8b	MaxText model: llama3.1-8b	maxtext_llama3.1-8b ✅	0.11.1
MaxText_llama3_3_70b	MaxText model: llama3.3-70b	maxtext_llama3.3-70b ✅	0.11.1
MaxText_llama3_405b	MaxText model: llama3-405b	maxtext_llama3-405b ✅	0.11.1
MaxText_llama3_70b	MaxText model: llama3-70b	maxtext_llama3-70b ✅	0.11.1
MaxText_llama3_8b	MaxText model: llama3-8b	maxtext_llama3-8b ✅	0.11.1
MaxText_mistral_7b	MaxText model: mistral-7b	maxtext_mistral-7b ✅	0.11.1
MaxText_olmo3_32b	MaxText model: olmo3-32b	maxtext_olmo3-32b ✅	0.11.1
MaxText_olmo3_7b	MaxText model: olmo3-7b	maxtext_olmo3-7b ✅	0.11.1
MaxText_olmo3_7b_pt	MaxText model: olmo3-7b-pt	maxtext_olmo3-7b-pt ✅	0.11.1
MaxText_qwen3_0_6b	MaxText model: qwen3-0.6b	maxtext_qwen3-0.6b ✅	0.11.1
MaxText_qwen3_14b	MaxText model: qwen3-14b	maxtext_qwen3-14b ✅	0.11.1
MaxText_qwen3_235b_a22b	MaxText model: qwen3-235b-a22b	maxtext_qwen3-235b-a22b ✅	0.11.1
MaxText_qwen3_30b_a3b	MaxText model: qwen3-30b-a3b	maxtext_qwen3-30b-a3b ✅	0.11.1
MaxText_qwen3_32b	MaxText model: qwen3-32b	maxtext_qwen3-32b ✅	0.11.1
MaxText_qwen3_480b_a35b	MaxText model: qwen3-480b-a35b	maxtext_qwen3-480b-a35b ✅	0.11.1
MaxText_qwen3_4b	MaxText model: qwen3-4b	maxtext_qwen3-4b ✅	0.11.1
MaxText_qwen3_4b_thinking_2507	MaxText model: qwen3-4b-thinking-2507	maxtext_qwen3-4b-thinking-2507 ✅	0.11.1
MaxText_qwen3_8b	MaxText model: qwen3-8b	maxtext_qwen3-8b ✅	0.11.1
MaxText_qwen3_next_80b_a3b	MaxText model: qwen3-next-80b-a3b	maxtext_qwen3-next-80b-a3b ✅	0.11.1
MaxText_qwen3_omni_30b_a3b	MaxText model: qwen3-omni-30b-a3b	maxtext_qwen3-omni-30b-a3b ✅	0.11.1
AutoEncoder	A simple autoencoder example (converter pipeline).	simple_autoencoder ✅ simple_autoencoder_f64 ✅	0.2.0
CNN	A simple convolutional neural network (CNN).	simple_cnn_static ✅ simple_cnn_dynamic ✅	0.2.0
DepthToSpaceResNet	Residual conv stack followed by dm_pix.depth_to_space upsampling.	depth_to_space_resnet_static ✅ depth_to_space_resnet_inputs_outputs_as_nchw ✅ depth_to_space_resnet_inputs_outputs_as_nchw_dynamic_hw ✅ depth_to_space_resnet_scaled_inputs_outputs_as_nchw ✅	0.11.2
ExclusiveSelfAttention	An XSA-style attention block that removes the component of the attention output aligned with the token's own value vector.	exclusive_self_attention ✅ exclusive_self_attention_opset23 ✅	0.12.4
ForiLoop	fori_loop example using nnx-compatible primitives (converter).	fori_loop_counter ✅ fori_loop_counter_f64 ✅ fori_loop_counter_custom_io_names ✅ fori_loop_counter_custom_io_names_f64 ✅ fori_loop_counter_custom_io_names_two_inputs ✅ fori_loop_counter_custom_io_names_two_inputs_f64 ✅	0.5.1
GRUCell	Flax/nnx GRUCell lowered through converter primitives.	gru_cell_basic ✅	0.7.2
MLP	A simple Multi-Layer Perceptron (MLP) with BatchNorm, Dropout, and GELU activation.	simple_mlp_static ✅ simple_mlp_static_f64 ✅ simple_mlp_dynamic ✅ simple_mlp_dynamic_f64 ✅ simple_mlp_with_call_params_dynamic ✅ simple_mlp_with_call_params_dynamic_f64 ✅ simple_mlp_with_call_params ✅ simple_mlp_with_call_params_f64 ✅	0.1.0
MultiHeadAttention	nnx.MultiHeadAttention exercised in several configurations, including custom attention_fn and symbolic batch variants.	multihead_attention_nn_dynamic ✅ multihead_attention_nn ✅ multihead_attention_nnx_dynamic ✅ multihead_attention_nnx ✅ multihead_attention_2_nnx_dynamic ✅ multihead_attention_2_nnx ✅ multihead_attention_gqa_nnx_dynamic ✅ multihead_attention_gqa_nnx ✅	0.2.0
NestedResidualGroup	Nested residual blocks inside a residual group; regression harness for issue #173.	nested_residual_group_static ✅ nested_residual_group_static_nchw ✅ nested_residual_group_with_lead_conv_static ✅ nested_residual_stack_static ✅ nested_residual_stack_static_no_extra_transpose ✅ nested_residual_stack_with_lead_conv_static ✅	0.12.0
ResBlock	Residual block with squeeze-and-excite channel attention (from issue #168).	resblock_channel_attention_static ✅ resblock_channel_attention_static_nchw ✅ resblock_channel_attention_dynamic_hw ✅ resblock_channel_attention_dynamic_hw_nchw ✅	0.12.0
SequentialReLU	Two stateless nnx.relu activations chained via nnx.Sequential.	sequential_double_relu ✅ sequential_double_relu_f64 ✅	0.7.1
SequentialWithResidual	nnx.Sequential nested within a residual block to regress earlier bugs.	sequential_nested_with_residual ✅	0.7.1
SimpleModel	Minimal NNX model that applies jnp.clip.	simple_model_clip_nhwc ✅ simple_model_clip_nchw_io ✅	0.12.0
TransformerDecoderWithSequential	Tiny nnx Transformer decoder using nnx.Sequential in the FFN block.	tiny_decoder_with_sequential ✅ tiny_decoder_with_sequential_and_full_dynamic_shapes_dynamic ✅	0.7.1
TransformerDecoderWithoutSequential	Tiny nnx Transformer decoder with explicit FFN layers (no Sequential).	tiny_decoder_without_sequential ✅	0.7.1
FlaxDINOv3VisionTransformer	DINOv3 Vision Transformer	nnx_dinov3_vit_Ti14_dynamic ✅ nnx_dinov3_vit_Ti14 ✅ nnx_dinov3_vit_S14_dynamic ✅ nnx_dinov3_vit_S14 ✅ nnx_dinov3_vit_B14_dynamic ✅ nnx_dinov3_vit_B14 ✅ nnx_dinov3_vit_S16_dynamic ✅ nnx_dinov3_vit_S16 ✅	0.10.3
NnxDinoAttention	Multi-Head Self-Attention using Flax/NNX modules.	nnx_attention_dynamic ✅ nnx_attention ✅	0.10.3
NnxDinoAttentionCore	Multi-Head Self-Attention without rotary processing.	nnx_attention_core_dynamic ✅ nnx_attention_core ✅	0.10.3
NnxDinoBlock	Transformer Block.	nnx_transformer_block_dynamic ✅ nnx_transformer_block ✅	0.10.3
NnxDinoPatchEmbed	Image to Patch Embedding.	nnx_patch_embed ✅	0.10.3
FlaxAttentionBlock	Attention block from the GPT-OSS Flax reference (no KV cache).	gpt_oss_attention_flax ✅	0.10.2
FlaxMLPBlock	Mixture-of-experts MLP block from the GPT-OSS Flax port.	gpt_oss_mlp_flax ✅	0.10.2
FlaxRMSNorm	Flax RMSNorm used in the GPT-OSS JAX port.	gpt_oss_rmsnorm_flax_dynamic ✅ gpt_oss_rmsnorm_flax ✅	0.10.2
FlaxRotaryEmbedding	Rotary position embedding helper from the GPT-OSS Flax port.	gpt_oss_rotary_flax ✅	0.10.2
FlaxSDPA	JIT sdpa helper from the GPT-OSS Flax port.	gpt_oss_sdpa_flax ✅	0.10.2
FlaxTransformer	Full GPT-OSS Flax transformer (embedding, blocks, head).	gpt_oss_transformer_flax ✅	0.10.2
FlaxTransformerBlock	Single GPT-OSS Flax transformer block (attention + MoE MLP).	gpt_oss_transformer_block_flax ✅	0.10.2
onnx_functions_000	One function boundary on an outer NNX module (new-world).	000_one_function_on_outer_layer_dynamic ✅ 000_one_function_on_outer_layer ✅	0.4.0
onnx_functions_001	one function on an inner layer.	001_one_function_inner_dynamic ✅ 001_one_function_inner ✅	0.4.0
onnx_functions_002	two nested functions.	002_two_nested_functions_dynamic ✅ 002_two_nested_functions ✅	0.4.0
onnx_functions_003	two nested functions.	003_two_simple_nested_functions_dynamic ✅ 003_two_simple_nested_functions ✅	0.4.0
onnx_functions_004	nested function plus component	004_nested_function_plus_component_dynamic ✅ 004_nested_function_plus_component ✅	0.4.0
onnx_functions_005	nested function plus more components	005_nested_function_plus_component_dynamic ✅ 005_nested_function_plus_component ✅	0.4.0
onnx_functions_006	one function on an outer layer.	006_one_function_outer_dynamic ✅ 006_one_function_outer ✅	0.4.0
onnx_functions_007	transformer block with nested mlp block with call parameter	007_transformer_block_dynamic ✅ 007_transformer_block ✅	0.4.0
onnx_functions_008	transformer block with nested mlp block no call parameter	008_transformer_block_dynamic ✅ 008_transformer_block ✅	0.4.0
onnx_functions_009	transformer block using decorator on class and function	009_transformer_block_dynamic ✅ 009_transformer_block ✅	0.4.0
onnx_functions_010	transformer stack	010_transformer_stack_dynamic ✅ 010_transformer_stack ✅	0.4.0
onnx_functions_012	Vision Transformer (ViT)	012_vit_conv_embedding_dynamic ✅ 012_vit_conv_embedding ✅	0.4.0
onnx_functions_013	Vision Transformer (ViT)	013_vit_conv_embedding_with_call_params_dynamic ✅ 013_vit_conv_embedding_with_call_params ✅ 013_vit_conv_embedding_with_internal_call_params_dynamic ✅ 013_vit_conv_embedding_with_internal_call_params ✅	0.4.0
onnx_functions_014	one function on an outer layer.	014_one_function_with_input_param_with_default_value ✅ 014_one_function_without_input_param_with_default_value_dynamic ✅ 014_one_function_without_input_param_with_default_value ✅	0.4.0
onnx_functions_015	one function on an outer layer.	015_one_function_with_input_param_without_default_value_dynamic ✅ 015_one_function_with_input_param_without_default_value ✅	0.4.0
onnx_functions_016	nested function plus more components	016_internal_function_with_input_param_with_default_value_dynamic ✅ 016_internal_function_with_input_param_with_default_value ✅	0.4.0
onnx_functions_017	Demonstrates @onnx_function(unique=True) reuse across call sites.	017_unique_function_reuse ✅	0.10.0
ClassificationHead	Classification head for Vision Transformer	vit_classification_head_dynamic ✅ vit_classification_head ✅	0.4.0
ClassificationHeadFlatten	Classification head for Vision Transformer	vit_classification_head_flat_dynamic ✅ vit_classification_head_flat ✅	0.4.0
ConcatClsToken	Concatenate CLS token to the input embedding	vit_concat_cls_token_dynamic ✅ vit_concat_cls_token ✅	0.4.0
ConcatClsTokenFlatten	Concatenate CLS token to the input embedding	vit_concat_cls_token_flat_dynamic ✅ vit_concat_cls_token_flat ✅	0.4.0
ConvEmbedding	Convolutional Token Embedding for MNIST with hierarchical downsampling.	vit_mnist_conv_embedding_dynamic ✅ vit_mnist_conv_embedding ✅	0.1.0
ConvEmbeddingFlatten	Convolutional Token Embedding for MNIST with hierarchical downsampling.	vit_mnist_conv_embedding_flat_dynamic ✅ vit_mnist_conv_embedding_flat ✅	0.1.0
FeedForward	MLP in Transformer	vit_feed_forward_dynamic ✅ vit_feed_forward ✅	0.1.0
FeedForwardFlatten	MLP in Transformer	vit_feed_forward_flat_dynamic ✅ vit_feed_forward_flat ✅	0.1.0
GetToken	Get the CLS token from the input embedding	vit_get_token_dynamic ✅ vit_get_token ✅	0.4.0
GetTokenFlatten	Get the CLS token from the input embedding	vit_get_token_flat_dynamic ✅ vit_get_token_flat ✅	0.4.0
PatchEmbedding	Cutting the image into patches and linearly embedding them.	vit_patch_embedding_dynamic ✅ vit_patch_embedding ✅	0.1.0
PatchEmbeddingFlatten	Cutting the image into patches and linearly embedding them.	vit_patch_embedding_flat_dynamic ✅ vit_patch_embedding_flat ✅	0.1.0
PositionalEmbedding	Add positional embedding to the input embedding	vit_positional_embedding_dynamic ✅ vit_positional_embedding ✅	0.4.0
PositionalEmbeddingFlatten	Add positional embedding to the input embedding	vit_positional_embedding_flat_dynamic ✅ vit_positional_embedding_flat ✅	0.4.0
TransformerBlock	Transformer from 'Attention Is All You Need.'	vit_transformer_block_dynamic ✅ vit_transformer_block ✅	0.1.0
TransformerBlockFlatten	Transformer from 'Attention Is All You Need.'	vit_transformer_block_flat_dynamic ✅ vit_transformer_block_flat ✅	0.1.0
TransformerStack	Stack of Transformer blocks	vit_transformer_stack_dynamic ✅ vit_transformer_stack ✅	0.1.0
TransformerStackFlatten	Stack of Transformer blocks	vit_transformer_stack_flat_dynamic ✅ vit_transformer_stack_flat ✅	0.1.0
VisionTransformer	A Vision Transformer (ViT) model for MNIST with configurable embedding type.	vit_model_conv_embedding_dynamic ✅ vit_model_conv_embedding ✅ vit_model_patch_embedding ✅	0.2.0
VisionTransformerFlatten	A Vision Transformer (ViT) model for MNIST with configurable embedding type.	vit_model_conv_embedding_flat_dynamic ✅ vit_model_conv_embedding_flat ✅ vit_model_patch_embedding_flat_dynamic ✅ vit_model_patch_embedding_flat ✅	0.2.0