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THE CONSCIOUSNESS ATLAS
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ConsciousnessLearn · Explore · Joseph Anady

tpool_patch_merger: reshaped seq

In tpool_patch_merger, this statement defines or updates reshaped_seq. The reduction aggregates values along the explicit source axis; preserving the axis and keep-dimension setting matters for the later broadcast.

Donor model implementationhistorical source recovered; expression not executed in this website audit
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donor-eq-993a6c46e6295asource-code mathematical expression
reshapedseq=reshapedseq.permute(0,1,3,2,4,5).contiguous().mean(dim=0)reshaped_seq = reshaped_seq.permute(0, 1, 3, 2, 4, 5).contiguous().mean( dim=0)

Read the equation

In tpool_patch_merger, this statement defines or updates reshaped_seq. The reduction aggregates values along the explicit source axis; preserving the axis and keep-dimension setting matters for the later broadcast.

Terms & definitions

The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.

How it works

  1. Read reshaped_seq as the quantity being defined or updated by this relation.

  2. Substitute the source-defined variables or recorded values and follow the displayed arithmetic and indexing exactly.

  3. Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.

Assumptions & limits

Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.

Original context

Read the surrounding source passage
new_height, new_width = h // kernel_height, w // kernel_width
        reshaped_seq = seq.view(t, new_height, kernel_height, new_width,
                                kernel_width, d_model)
        reshaped_seq = reshaped_seq.permute(0, 1,
                                            3, 2, 4, 5).contiguous().mean(
                                                dim=0)  # temporal pooling
        padded_seq = reshaped_seq.view(new_height * new_width,
                                       kernel_height * kernel_width, -1)
        outputs.append(padded_seq)
Exact original notation
reshaped_seq = reshaped_seq.permute(0, 1,
                                            3, 2, 4, 5).contiguous().mean(
                                                dim=0)

Related builds

Sources & provenance

01
kimi source tree.txt
lines 1671–1673 · Aug 5, 2026
reshaped_seq = reshaped_seq.permute(0, 1, 3, 2, 4, 5).contiguous().mean( dim=0)

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