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ci/official/wheel_test/README.md
the local wheel file specified in the `requirements_lock_<python_version>.txt`. Packages are imported one by one in alphabetical order during runtime. The test doesn't identify package's order-dependent issues; for instance, importing "tf.foo" followed by "tf.bar" won't reveal that "tf.bar" depends on "tf.foo" being imported first.
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tensorflow/c/eager/abstract_operation.h
// clients from directly destroying this object since it may manage it's own // lifetime through ref counting. Thus this must be allocated on the heap and // clients MUST call Release() in order to destroy an instance of this class. virtual void Release() = 0; virtual Status Reset(const char* op, const char* raw_device_name) = 0; virtual const string& Name() const = 0;
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tensorflow/c/experimental/filesystem/plugins/gcs/ram_file_block_cache.h
/// be accessed while holding the Block's mu lock. The data vector should only /// be accessed after state == FINISHED, and it should never be modified. /// /// In order to prevent deadlocks, never grab the block-cache-wide mu_ lock /// AFTER grabbing any block's mu lock. It is safe to grab mu without locking /// mu_. struct Block { /// The block data.
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tensorflow/api_template.__init__.py
this file with a file generated from [`api_template.__init__.py`](https://www.github.com/tensorflow/tensorflow/blob/master/tensorflow/api_template.__init__.py) """ # pylint: disable=g-bad-import-order,protected-access,g-import-not-at-top import distutils as _distutils import importlib import inspect as _inspect import os as _os import site as _site import sys as _sys
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tensorflow/c/eager/c_api_unified_experimental.cc
return absl::OkStatus(); } string msg = absl::StrCat( "No tracing engine factory has been registered with the key '", name, "' (available: "); // Ensure deterministic (sorted) order in the error message std::set<string> factories_sorted; for (const auto& factory : GetFactories()) factories_sorted.insert(factory.first); const char* comma = ""; for (const string& factory : factories_sorted) {
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tensorflow/c/experimental/filesystem/plugins/gcs/ram_file_block_cache.cc
} else { // Remove the stale block and continue. RemoveFile_Locked(key.first); } } // Insert a new empty block, setting the bookkeeping to sentinel values // in order to update them as appropriate. auto new_entry = std::make_shared<Block>(); lru_list_.push_front(key); lra_list_.push_front(key); new_entry->lru_iterator = lru_list_.begin();
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tensorflow/c/eager/c_api.h
// can correspond to the data contained in another dimension in on-host // representation. The dimensions are indexed using the standard TensorFlow // major-to-minor order (slowest varying dimension first), // not the XLA's minor-to-major order. // On-device dimensions can be padded. TFE_TensorDebugInfoOnDeviceDim returns // the number of elements in a dimension after padding.
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tensorflow/BUILD
# Do not sort: Copybara rule # copybara:uncomment_begin # # buildifier: disable=out-of-order-load # load("//devtools/build_cleaner/skylark:action_config_test.bzl", "action_config_test") # load("//devtools/copybara/rules:copybara.bzl", "copybara_config_test") # load("@rules_license//rules:license.bzl", "license") # # buildifier: enable=out-of-order-load # copybara:uncomment_end # copybara:comment_begin(oss-only)
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tensorflow/c/eager/abstract_context.h
// // WARNING: The destructor of this class is marked as protected to disallow // clients from directly destroying this object since it may manage its own // lifetime through ref counting. Thus clients MUST call Release() in order to // destroy an instance of this class. virtual void Release() = 0; // Creates an operation builder and ties it to this context. // The returned object can be used for setting operation's attributes,
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RELEASE.md
semantics to match NumPy. * `tf.split` now takes arguments in a reversed order and with different keywords. In particular, we now match NumPy order as `tf.split(value, num_or_size_splits, axis)`. * `tf.sparse_split` now takes arguments in reversed order and with different keywords. In particular we now match NumPy order as `tf.sparse_split(sp_input, num_split, axis)`. NOTE: we have
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