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ci/official/requirements_updater/README.md
To set a different version, use the `TF_PYTHON_VERSION` environment variable, e.g. ``` export TF_PYTHON_VERSION=3.11 ``` To specify the version via a Bazel command argument, use the following: ``` --repo_env=TF_PYTHON_VERSION=3.11 ``` ## Requirements updater Requirements updater is a standalone tool, intended to simplify process of
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tensorflow/c/experimental/filesystem/filesystem_interface.h
/// `const`. /// /// For consistency, the arguments on all these functions follow the same /// pattern: first we have the opaque pointer argument ("this" above), then the /// input arguments, then the in-out arguments (if any) and we finish the /// argument list with the out arguments. We only use the return type for an out /// parameter if that is a plain C type, as this ensures ABI compatibility
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.github/workflows/release-branch-cherrypick.yml
# input the branch name and paste the cherry-pick commit and click Run. A PR # will be created. name: Release Branch Cherrypick on: workflow_dispatch: inputs: # We use this instead of the "run on branch" argument because GitHub looks # on that branch for a workflow.yml file, and we'd have to cherry-pick # this file into those branches. release_branch: description: 'Release branch name (e.g. r2.9)'
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tensorflow/c/c_api_function_test.cc
DefineFunction("MyGrad2", &grad_func2); // func cannot be null TF_GraphCopyFunction(host_graph_, nullptr, func_, s_); EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_)); EXPECT_EQ(string("'func' argument to TF_GraphCopyFunction cannot be null"), string(TF_Message(s_))); // Cannot change gradient TF_GraphCopyFunction(host_graph_, func_, grad_func1, s_);
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tensorflow/c/eager/immediate_execution_operation.h
virtual ImmediateExecutionContext* GetContext() const = 0; // Following two methods are used to support custom device. // Return true if the inputs contain custom device tensor handle. It means // that the argument need to be handled by a custom device. virtual bool HasCustomDeviceInput() const = 0; virtual const tensorflow::OpDef* OpDef() const = 0; virtual Status InputLength(const char* input_name, int* length) = 0;
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tensorflow/c/experimental/filesystem/plugins/gcs/gcs_filesystem.cc
} else { return TF_SetStatus(status, TF_INVALID_ARGUMENT, "Not a directory!"); } }; GcsFileStat stat; MaybeAppendSlash(&dir); gcs_file->stat_cache->LookupOrCompute(dir, &stat, compute_func, status); if (TF_GetCode(status) != TF_OK && TF_GetCode(status) != TF_INVALID_ARGUMENT) return false; if (TF_GetCode(status) == TF_INVALID_ARGUMENT) { TF_SetStatus(status, TF_OK, ""); return false;
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RELEASE.md
* Extends `tf.keras.layers.Lambda` layers to support multi-argument lambdas, and keyword arguments when calling the layer. * Functional models now get constructed if *any* tensor in a layer call's arguments/keyword arguments comes from a keras input. Previously the functional api would only work if all of the elements in the first argument to the layer came from a keras input.
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tensorflow/c/c_api_test.cc
TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, 5, s); EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s); // Try to set an invalid shape (cannot change 2x3 to a 2x5). dims[1] = 5; TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s); EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s); // Test for a scalar. TF_Operation* three = ScalarConst(3, graph, s);
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tensorflow/c/experimental/filesystem/plugins/gcs/cleanup.h
==============================================================================*/ // MakeCleanup(f) returns an RAII cleanup object that calls 'f' in its // destructor. The easiest way to use MakeCleanup is with a lambda argument, // capturing the return value in an 'auto' local variable. Most users will not // need more sophisticated syntax than that. // // Example: // void func() { // FILE* fp = fopen("data.txt", "r");
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tensorflow/c/eager/parallel_device/parallel_device.cc
" outputs for TPUReplicatedOutput, but got ", expected_outputs)); TF_SetStatus(status, TF_INVALID_ARGUMENT, message.c_str()); return result; } if (absl::holds_alternative<TFE_TensorHandle*>(inputs[0])) { TF_SetStatus(status, TF_INVALID_ARGUMENT, "Expected the input to " "TPUReplicatedOutput to be a parallel tensor (placed on the "
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